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Pain rates in general population for the period 1991–2015 and 10-years prediction: results from a multi-continent age-period-cohort analysis

The Journal of Headache and Pain volume 21, Article number: 52 (2020) Cite this article

Abstract

Background

Pain is a common symptom, often associated with neurological and musculoskeletal conditions, and experienced especially by females and by older people. The aims of this study are to evaluate the temporal variations of pain rates among general populations for the period 1991–2015 and to project 10-year pain rates.

Methods

We used the harmonized dataset of ATHLOS project, which included 660,028 valid observations in the period 1990–2015 and we applied Bayesian age–period–cohort modeling to perform projections up to 2025. The harmonized Pain variable covers the content “self-reported pain experienced at the time of the interview”, with a dichotomous (yes or no) modality.

Results

Pain rates were higher among females, older subjects, in recent periods, and among observations referred to cohorts of subjects born between the 20s and the 60s. The 10-year projections indicate a noteworthy increase in pain rates in both genders and particularly among subjects aged 66 or over, for whom a 10–20% increase in pain rate is foreseen; among females only, a 10–15% increase in pain rates is foreseen for those aged 36–50.

Conclusions

Projected increase in pain rates will require specific interventions by health and welfare systems, as pain is responsible for limited quality of subjective well-being, reduced employment rates and hampered work performance. Worksite and lifestyle interventions will therefore be needed to limit the impact of projected higher pain rates.

Introduction

Pain is defined as “a distressing experience associated with actual or potential tissue damage with sensory, emotional, cognitive, and social components” [1] and is one the most common symptoms. It can be associated with many conditions, including some highly prevalent and disabling ones, such as headaches, musculoskeletal diseases or injuries, but also to sequelae of other disorders, such as diabetes [2]. These conditions often have long-lasting duration: they begin in adulthood, endure for several years and often worsen, and are associated with disability [3,4,5]. Pain rates are expected to rise due to the increased prevalence of musculoskeletal and neurological diseases, increased life expectancy and increased proportion of elderly in societies [6].

The results of epidemiological studies show that the prevalence of pain-associated conditions is between 10% and 40% approximately [7,8,9,10,11,12,13,14,15,16,17]: the only exception to this is for the one-year prevalence of tension-type headache, which peaked up to 60% [13,14,15]. Literature also show that pain is more frequently experienced by females and by older people, which is consistent with the epidemiological presentation of the main drivers of pain, i.e. headache and musculoskeletal disorders. Based on these figures, it could be hypothesized that most of the general population would report pain as a daily or near-daily problem. However, reliable information on the presence and time trend of pain in the general population, irrespective of underlying health conditions, is limited to few studies. A recent one, which analyzed 18-year trends in the overall rates of noncancer pain prevalence in the U.S., showed that the proportion of adults reporting pain due to painful health conditions increased from 32.9% to 41.0% between 1997 and 98 and 2013–14 [18].

Understanding such a trend, as well as the projections towards future periods, is of relevance for the management of pain as a symptom, particularly for the risk of overuse of medications such as opioids [18,19,20]. In addition to this, issues connected to worse health outcome, e.g. higher disability, reduced employment rates and loss of productivity are also of relevance [21,22,23,24,25,26,27,28]. This is, in turn, an important determinant of economic burden, which is high because of both per-case cost and general prevalence of pain-related conditions [29,30,31,32,33]. However, while higher pain rates among females and among older subjects, and occasionally differences by period have been shown, with higher pain rates in recent periods [6, 18], information on pain rates by cohort is basically lacking. The integration of gender, age, period and cohort data is of core importance to produce projections on pain rates in the general population.

The aims of this study are to evaluate the temporal variations of pain rates among the general populations for the period 1991–2015 and to predict future rates up to 2025. These variations were analyzed by gender considering age, periods (year of survey) and cohorts (birth year), as derived by the ATHLOS project (Ageing Trajectories of Health – Longitudinal Opportunities and Synergies) harmonized dataset, which includes data collected in different studies carried out in the five continents.

Materials and methods

ATHLOS project and the harmonized dataset

ATHLOS project was funded by the European Union’s Horizon 2020 Research and Innovation Program, and it aims to achieve a better understanding of the impact of ageing on health. To achieve this result, a new cohort has been composed from harmonized datasets of existing international longitudinal cohorts related to health and ageing (see: https://github.com/athlosproject/athlos-project.github.io). The harmonized dataset includes records of participants from 17 different studies and is fully described elsewhere [34]: most of these studies were run between 2000 and 2010 and have at least two waves, but there are both older and more recent ones, as well as studies with one wave only. The harmonized ATHLOS mega-dataset comprises approximately 411,000 respondents. Most of the studies whose data are included in ATHLOS dataset were from high-income countries and upper-middle-income countries: the only exceptions are India and Ghana.

ATHLOS harmonized dataset is composed of a wide range of variables covering a variety of health conditions, sociodemographic variables, personal functioning and contextual factors, which are usually assessed in population studies. The ATHLOS dataset variables were classified in the following domains: sociodemographic and economic characteristics, lifestyle and health behaviors, health status and functional limitations, diseases, living status, physical measures, psychological measures, laboratory measures, social environment and life events, and administrative information [34].

Pain is included within health status and functional limitations and was measured in 14 out of the 17 studies, with different approaches (please refer to supplementary materials for information on the distribution of pain variable across studies and countries). Some studies, e.g. the Collaborative Research on Ageing in Europe [35] and the China Health and Retirement Longitudinal Study [36], addressed it in terms of pain severity (i.e. None, Mild, Moderate, Severe, Extreme or other similar formats). Other studies, e.g. the Australian Longitudinal Study of Aging [37] and the Survey of Health, Ageing and Retirement in Europe [38], dichotomously addressed the presence of pain (i.e. yes or no), sometimes addressing the idea that pain is “often experienced”, such as in the Irish Longitudinal Study of Ageing [39]. The harmonization procedure is aimed to generate inferentially equivalent content across studies so to make the content of variables collected in different studies uniform. In the case of pain variable, the content was “self-reported pain experienced at the time of the interview”, and the variable modality was dichotomous.

Age period cohort analysis

Age period cohort (APC) models are commonly used to analyze and project rates [40, 41]. APC models account for these processes on three time scales: age, year of survey (period) and year of birth (cohort). The period and cohort effects are both surrogates for exposure to external factors. Period effects include environmental and diagnostic factors. For example, the introduction of a new diagnostic procedure may lead to a jump in disease incidence across all age groups. Cohort effects represent risk factors that change over time and may have a delayed effect on disease outcomes. For example, lifestyle factors, such as alcohol and tobacco consumption, can manifest themselves as cohort effects [41].

Data were organized by five-year periods from 1991 to 2015, five-year age-groups from the group 31–35 to the 96–100 one and stratified by gender. In a preliminary analysis, age-specific trend rates by gender were computed. Trend rates vary between 0 and 1, with higher values indicating higher proportion of pain reporting in specific subgroups and observations, i.e. among males and females, by age, cohort and periods. Later, APC models were fitted to analyze the joint effects of the age, period, and cohort, expressed in Rate Ratio (RR) terms. RR is a relative measure that allows to identify potential protective (if RR < 1) and risk factors (if RR > 1), in this case related to particular ages, periods or cohorts. Pain counts yijk in age group i at time point j in kth cohort can be assumed to be Poisson distributed, i.e.,

$$ {\mathrm{y}}_{\mathrm{ijk}}=\mathrm{Po}\left({\mathrm{n}}_{\mathrm{ijk}}{\uplambda}_{\mathrm{ijk}}\right),\mathrm{where}\ \mathrm{i}=1,\dots, \mathrm{I};\mathrm{j}=1,\dots, \mathrm{J};\mathrm{k}=1,\dots, \mathrm{K}, $$

with mean nijkλijk, where nijk denotes the corresponding subjects at risk. In our application, the age index i run from 1 to I = 14 (both in males and females), while the period index j run from 1 to J = 5. Concerning the cohort index k, it depended on the age group and period index, but also on their intervals width [42, 43] and it was defined as M × (I − i) + j, where M indicated the ratio between the width in years of the age group and the period intervals [44]. In our application M was equal to [5 years] / [5 years] = 1, and the cohort index k, following the previous formula where the age groups were fourteen and the periods are five, generated K = 1 × (14–1) + 5 = 18 cohorts, i.e., the cohort index run from 1 to K = 18, but only 9 not overlapping (i.e., 1891–1900, 1901–1910,…,1971–1980). Given that, the model was specified as

$$ \mathrm{Log}\left({\uplambda}_{\mathrm{i}\mathrm{jk}}\right)={\upmu}_{\mathrm{i}\mathrm{jk}}=\upmu +{\upalpha}_{\mathrm{i}}+{\upbeta}_{\mathrm{j}}+{\upgamma}_{\mathrm{k}} $$

Here μ represented the general level (intercept), and αi, βj, γk denoted age, period, and cohort random effects (to be estimated), respectively [45].

A fully Bayesian approach based on Integrated Nested Laplace Approximations (INLA) was considered for model fitting and inference [46]. A Bayesian APC model provides a more robust methodology compared to a log-linear model, particularly for the prediction of future occurrence [47,48,49]. Indeed, in our case, Bayesian age–period–cohort modeling was also used to perform projections, and relative credible intervals (CI), of the pain symptom rates in the time interval 2015–2025, extrapolating the trend of rate from 1990 to 2015 and considering the subjects at risk relative to the last period 2011–2015. For each projection, different degrees of CI were reported, the closer to the predicted mean being 10% CI and the largest being 95% CI.

The model had some a priori assumptions: (i) log-RR for each effect summed to zero over the observed interval; (ii) the expected effects were hypothesized to be constant, so that both the large and the small deviations from a constant rate are detected. The Bayesian APC model also considered hyper-parameters of random walk type of first (RW1) and second order (RW2) [50] on which log-gamma prior distributions were elicited. We intentionally used highly non-informative log-gamma prior distributions (with parameters equal to 1 and 0.00005) in order to endorse and make more credibility to harmonized ATHLOS dataset and avoid the imposition of assumptions for which no a priori knowledge was available. A prior distribution on an overdispersion parameter was also elicited by an independent log-gamma with parameters equal to 1 and 0.005.

Parameter estimates in terms of mean and median were obtained. Finally, to select the best model among the different proposals, the Deviance Information Criterion (DIC, lower was better) [51] were computed. In particular, different specification as age period and age cohort models, and different combinations of priors of RW1 and RW2 [50] were probed by considering the random effects of age, period and cohort. In case RW1 and RW2 priors provided very similar DICs, we decided to select the complete model with RW2 priors because is a standard choice as natural target for smoothing [52, 53].

The technique was implemented in the software R v 3.5.2 [54] through the packages R-INLA (www.r-inla.org) [55,56,57] and Bayesian APC [50] for the projections.

Results

In total 660,028 valid observations were used, of whom 293,484 reported some degree of pain, the rate being 44.5% (see Table 1): the rate of pain was higher among observations referred to females than to males. As shown in Table 2, the average age of participants at the time of observation was comprised between 64 and 65 years across all periods, with the exception of the 1991–1995 one, where the average age was approximately 5 years below that of the other periods. Supplementary Tables S1-S4 report distribution of all observations and of observations with pain by period and by 5-years age groups; supplementary Tables S5-S8 report the same information not aggregated by 5-years periods.

Table 1 Presence of pain in the general population included in ATHLOS harmonized dataset by age class
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Table 2 Age by period
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Figure 1 reports the observed pain rates by age, period and cohort for males and females. Among males rates by age varied approximately between 0.3 and 0.4, increasing with age and remaining basically stable after 80 years of age; among females, rates by age varied approximately between 0.4 and 0.54, peaking at the age class 81–85 and declining thereafter. With regard to rates by periods, a consistent increase was observed in both genders: among males it varied between 0.2 and 0.4, among females between 0.3 and 0.5. Regarding rates by cohort, it has parabolic shapes in both genders: among males the peak was around 0.4 for the cohort born in 1941–1950, among females it was around 0.5 for the cohorts born between 1921 and 1950.

Fig. 1
figure 1

Observed rate of pain in males and females by age, period and cohort. a: pain rates by age. b: pain rates by period. c: pain rate by cohort. Note. The dark central line indicates rate = 0.5

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Age-specific rates by gender and per period are shown in Fig. 2. Rates were higher among females and, in both genders, were higher among older subjects. A trend related to the period was observed, in both genders and across all age groups, with participants recruited in more recent periods experiencing pain with a higher frequency. An exception to this is observed for participants aged below 50 for whom, in the period 2010–2015, a decline in trend was observed in both genders.

Fig. 2
figure 2

Observed pain rate in males and females in the ATHLOS dataset. a: age-specific pain rates by age for males. b: age-specific pain rates by age for females

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Figure 3 shows the results of the APC models. The effect of age was different among males and females: in fact, while a consistent increasing trend was observed in females, with age becoming a risk factor for pain in the seventh decade of life, the age effect in males decreased from the first age group up to the 50–55 group and then it increased, becoming a risk factor in the eighth decade of life. The period effect was constantly growing in both males and females and it became a risk factor for pain from the period 2006–2010 onwards. The cohort effect had a parabolic shape: being born between the 20s and the 60s was a risk factor for pain in both genders. The selected models for both males (DIC = 650.6) and females (DIC = 723.8) involved the complete age period cohort models and the choice of RW2 priors (see supplementary Table S9). It is worth to point out that the estimated decreasing trend for the youngest age groups in males could be due to the small number of periods in relation to the age groups. In order to account for that, we have also performed a sensitivity analysis where the periods were non-aggregated (data are included in supplemental materials, Tables S5-S8). The results of this last analysis were very similar to that of those carried out with aggregated data: only for the period effects differences on the trends that presented a more irregular increase were detected (see Fig. S1). Moreover, because of data sparsity, sensitivity analysis could not produce projection as estimates intervals would have been too wide and unstable.

Fig. 3
figure 3

Estimated effects of age, period and cohort in rate ratio terms by gender. a: estimated effect of age. b: estimated effect of period. c: estimated effect of cohort. Note. The dark central line indicates RR = 1

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Figures 4 and 5 show observed and predicted rates for all age groups and for males and females, respectively. For both males and females, the projection is indicative of an increase in pain rates between 2015 and 2025, and such an increase was wider among older subjects. Among males, the projected increase over the decade was higher than 10% in all age groups from the 66–70 one, and get close to 20% for the age groups 91–95 and 96–100 (Fig. 4m-n). The same applies to females, for whom however the projection over the decade was higher than 20% in the oldest group (Fig. 5n). Moreover, an increase comprised between 10% and 15% over the decade was also observed among females for the age groups comprised between 36 and 50 years (Fig. 5b-d).

Fig. 4
figure 4

Pain rates and 10-years pain rate projection per age group in males. a: pain rate projection, males, age 31–35. b: pain rate projection, males, age 36–40. c: pain rate projection, males, age 41–45. d: pain rate projection, males, age 46–50. e: pain rate projection, males, age 51–55. f: pain rate projection, males, age 56–60. g: pain rate projection, males, age 61–65. h: pain rate projection, males, age 66–70. i: pain rate projection, males, age 71–75. j: pain rate projection, males, age 76–80. k: pain rate projection, males, age 81–85. l: pain rate projection, males, age 86–90. m: pain rate projection, males, age 91–95. n: pain rate projection, males, age 96–100. Note. The predictive mean is shown as solid line. The different shadings indicate pointwise credible intervals. The central interval represents 10% CI, and the largest interval 95% CI. Observed rates are shown as a filled circle. The vertical dashed line indicates where prediction started

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Fig. 5
figure 5

Pain rates and 10-years pain rate projection per age group in females. a: pain rate projection, females, age 31–35. b: pain rate projection, females, age 36–40. c: pain rate projection, females, age 41–45. d: pain rate projection, females, age 46–50. e: pain rate projection, females, age 51–55. f: pain rate projection, females, age 56–60. g: pain rate projection, females, age 61–65. h: pain rate projection, females, age 66–70. i: pain rate projection, females, age 71–75. j: pain rate projection, females, age 76–80. k: pain rate projection, females, age 81–85. l: pain rate projection, females, age 86–90. m: pain rate projection, females, age 91–95. n: pain rate projection, females, age 96–100. Note. The predictive mean is shown as solid line. The different shadings indicate pointwise credible intervals. The central interval represents 10% CI, and the largest interval 95% CI. Observed rates are shown as a filled circle. The vertical dashed line indicates where prediction started

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Discussion

The results of this study confirm part of the finding of previous literature, namely higher rates of pain among females and among older subjects, and reinforce the few available data on period analysis by showing higher pain rates in more recent periods, with specific reference to the observations referred to older subjects. In addition to this, our results show that pain rates were higher among the observations referred to cohorts of subjects born between the 20s and the 60s (i.e. between 1921 and 1970). Finally, the most innovative aspect of our work lies in the 10-year projection for pain rates, which indicate a noteworthy increase in pain rates across all age groups and in both genders. Among males aged 66 and over, the increase is projected to be by 10–20% over the decade, and the same is for females: in addition to this, however, also females aged 36–50 will likely face a 10–15% increase in pain rates.

Taken as a whole, our results tell the story of an expansion of pain-related morbidity, irrespective of its aetiology. Chronic pain prevalence has been occasionally addressed, but available reports show that it is increasing with age and through the years as an effect of population ageing [58, 59]. In addition to the known effect of age, some reports have also shown that people enrolled in recent years in population studies tend to report higher pain rates, i.e. an effect of age [6, 18] which may underlie differences in pain reporting, that might be due to cultural or biological issues. Our data do not enable to make hypotheses on what is causing such a period effect, but it has to be acknowledged that the prevalence of some conditions is rising worldwide. Examples of this include diabetic neuropathy, which affects up to 50% of patients with diabetes [60] and is expected to increase in consideration of the rising prevalence of diabetes worldwide [61, 62], and musculoskeletal conditions, which are among the main pain drivers, and represent approximately 19% of non-communicable diseases in terms of prevalence and 20% in terms of disability [2].

In consideration of the higher impact of such conditions among older subjects, and in consideration of global population ageing, actions aimed to prevent and control pain are and will be more and more needed. It is interesting to notice the increase in pain rates which is specific for females aged 36–50. This age group is at higher risk of having migraine or tension-type headache [3, 63,64,65,66]. It has to be noted that, as shown by GBD data [3], the decrease in prevalence of headache disorders begins after the age of 30, but the decline is particularly pronounced after the age of 50, particularly among women; this may be the reason for the differential increase by gender in the 36–50 age group. In addition to this, there are biological differences that make women to be exposed to specific pain, such as cyclic menstrual pain or menstrual migraine, which affects 22% to 62% of female migraineurs [67, 68]. Such an increase in pain rates will also be accompanied by an increase in the amount of the portion of population in that age group (third to fifth decade of life) in high and upper-middle income countries. In fact, in high-income countries, the median age between 1990 and 2015, moved from 33.4 to 40.4, and is projected to span between 41.5 and 43.5 in 2020–2030; in upper-middle income countries, the median age between 1990 and 2015, moved from 24.5 to 33.9, and is projected to span between 35.6 and 39.9 in 2020–2030 [69]. Therefore, it can be hypothesized that headache disorders will contribute more and more to the overall burden of pain in general populations. In addition to this, headache disorders and back pain, which are among the most prevalent and disabling conditions [2], show considerable rates of comorbidity, with odds of association comprised between 1.55 and 8.00 [70].

It is reasonable to hypothesize that in the older age groups, the difference in the increase of pain might be associated to the higher impact of some skeletal conditions, such as osteoporosis, and to the associated higher fracture rates. In fact, osteoporosis is approximately three-fold more prevalent in women than in men [71], and women older than 60 years have a 44% lifetime risk of fractures, compared to 25% among men of the same age [72].

Increasing pain rates will most likely be associated with increased healthcare utilization and consumption of different kinds of analgesics [73,74,75], and in particularly opioids, which however may in turn produce negative health outcomes and a further increase of healthcare use [76, 77]. In addition to this, pain is associated with reduced work productivity and sick leave [22, 26, 27, 78,79,80,81], early retirement and reduced employment rates [21, 25, 28, 82, 83]. The old-age dependency ratio, i.e. the ratio of the population aged 65 years or over to the population aged 15–64 -- referred to as the number of dependants per 100 persons of working age -- has been constantly increasing and is projected to further increase in both high-income and upper-middle-income countries. In high-income countries the dependency ratio increased from 18.3 to 25.7 between 1990 and 2015, and is projected to span between 25.8 and 36.3 between 2020 and 2030; in upper-middle-income countries the increase was from 8.1 to 10.5 between 1990 and 2015, and the rate will span between 12.3 and 16.2 between 2020 and 2030 [69]. Thus, our projections are not only indicative of an increased risk of future worse health status, but also of higher healthcare expenditure and overall financial burden which the health and welfare systems of high and upper-middle income countries, i.e. countries from which most of observations included in ATHLOS dataset were drawn [34], likely will struggle to bear.

Interventions are therefore needed to prevent pain and limit the impact of projected higher pain rates, which should include worksite interventions [84,85,86,87,88,89], lifestyle interventions and control over prescribed drugs [89,90,91,92]. Worksite intervention should be aimed to limit work cessation as well as enhance work productivity, and should ideally to be tailored to the features of each individual. Practically, these interventions can be tailored to the features of specific disorders as well, and may thus involve specific actions. A synthesis of the most effective strategies included among available literature [84,85,86,87,88,89,90,91,92] is beyond the aims of this study, but some general considerations can be made. Evidence exist that effective interventions act upon different levels, i.e. both patients and the workplaces, and involve several stakeholders, including treating pain specialists, occupational physicians and employers, and the most commonly reported strategies include appropriate pharmacotherapy, provision of ergonomic furniture, patients’ education on pain management, relaxation/posture exercises. Lifestyle interventions are aimed to reduce factors contributing to pain exacerbation and pain triggers, and most of them target diet and physical activity, as well as appropriate drug prescription and adherence to treatment. Finally, preliminary research evidence is available on the effectiveness of behavioral treatments, alone or as add-on to medical ones, for chronic pain control [93,94,95]. More research is needed in this field, that seems however promising and could provide a substantial contribution to control pain experience and reduce the consumption of medications.

Some limitations have to be acknowledged. The age period cohort models are commonly used to analyse and project mortality or morbidity rates from health registers to routinely collect demographic rates. However, the data herein reported do not derive from registries but from cross-sectional and longitudinal surveys, harmonized in the ATHLOS dataset. This entails that the population is not the same across periods, and therefore the sample sizes were different. Moreover, our analysis did not consider population dynamics either. In addition to this, it has to be remembered that our analysis is based on a harmonized dataset, whose core definition of pain variable is “self-reported pain experienced at the time of the interview”, with a dichotomous output. This creates an important limitation, namely the fact that this variable does not account for two issues of relevance in pain experience. The first is the severity and impact of pain, which spans between mild and disabling; the second is the frequency with which pain is experienced, which might be daily or near-daily, such as pain due to musculoskeletal conditions, episodic with variable frequency, such as in the case of headache disorders, or occasional. Finally, we interpreted the results of pain trends in light of the trends that can be reasonably expected for musculoskeletal and headache disorders in reason of their prevalence in the general population. However, none of the original pain-related variables included specification of pain in terms of aetiology or location.

Conclusion

In conclusion, we reported data on the temporal variations of pain rates among the general population for the period 1991–2015 and predicted 10-years future rates. Our results are based on a very large international dataset, which included data from populations from the five continents. Results show that pain rates were higher among females and among older subjects, and that rates were also higher among the respondents enrolled in more recent periods. Finally we showed that the trends of pain are increasing, in particular among females and among older subjects, for whom a 10–20% increase is projected over the 10-year period.

Pain is strongly associated with reduced employment rates and hampered work performance which, in consideration of population ageing and projected increase of dependency ratio of older people on people of working age, will require specific actions by health and welfare systems. Worksite and lifestyle interventions will be needed to limit the impact of projected higher pain rates.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available due restrictions imposed by part of the owners (COURAGE in Europe, HAPIEE, ALSA, the Health 2000 and 2011 Surveys- Finland, ENRICA and the 10/66 study), but may be available from the corresponding author upon reasonable request and once consent form ATHLOS project intellectual property and dissemination board is obtained.

Abbreviations

AP:

Age Period Cohort

ATHLOS:

Ageing Trajectories of Health – Longitudinal Opportunities and Synergies

CI:

Credible Intervals

DIC:

Deviance Information Criterion

INLA:

Integrated Nested Laplace Approximations

RR:

Rate Ratio

RW1:

Random Walk type of first order

RW2:

Random Walk type of second order

References

  1. Williams AC, Craig KD (2016) Updating the definition of pain. Pain. 157:2420–2423

    Article  PubMed  Google Scholar 

  2. GBD (2017) Disease and Injury Incidence and Prevalence Collaborators (2018) Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392:1789–1858

    Google Scholar 

  3. GBD (2016) Headache Collaborators (2018) Global, regional, and national burden of migraine and tension-type headache, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol 17:954–976

    Google Scholar 

  4. GBD (2016) Traumatic Brain Injury and Spinal Cord Injury Collaborators (2019) Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol 18:56–87

    Google Scholar 

  5. Sebbag E, Felten R, Sagez F, Sibilia J, Devilliers H, Arnaud L (2019) The world-wide burden of musculoskeletal diseases: a systematic analysis of the World Health Organization burden of diseases database. Ann Rheum Dis 78:844–848

    Article  PubMed  Google Scholar 

  6. Ahacic K, Kareholt I (2010) Prevalence of musculoskeletal pain in the general Swedish population from 1968 to 2002: age, period, and cohort patterns. Pain. 151:206–214

    Article  PubMed  Google Scholar 

  7. Failde I, Dueñas M, Ribera MV, Gálvez R, Mico JA, Salazar A et al (2018) Prevalence of central and peripheral neuropathic pain in patients attending pain clinics in Spain: factors related to intensity of pain and quality of life. J Pain Res 11:1835–1847

    Article  PubMed  PubMed Central  Google Scholar 

  8. Chenaf C, Delorme J, Delage N, Ardid D, Eschalier A, Authier N (2018) Prevalence of chronic pain with or without neuropathic characteristics in France using the capture-recapture method: a population-based study. Pain. 159:2394–2402

    Article  PubMed  Google Scholar 

  9. French HP, Smart KM, Doyle F (2017) Prevalence of neuropathic pain in knee or hip osteoarthritis: a systematic review and meta-analysis. Semin Arthritis Rheum 47:1–8

    Article  PubMed  Google Scholar 

  10. Mansfield KE, Sim J, Jordan JL, Jordan KP (2016) A systematic review and meta-analysis of the prevalence of chronic widespread pain in the general population. Pain. 157:55–64

    Article  PubMed  Google Scholar 

  11. Burch RC, Loder S, Loder E, Smitherman TA (2015) The prevalence and burden of migraine and severe headache in the United States: updated statistics from government health surveillance studies. Headache. 55:21–34

    Article  PubMed  Google Scholar 

  12. Mundal I, Gråwe RW, Bjørngaard JH, Linaker OM, Fors EA (2014) Prevalence and long-term predictors of persistent chronic widespread pain in the general population in an 11-year prospective study: the HUNT study. BMC Musculoskelet Disord 15:213

    Article  PubMed  PubMed Central  Google Scholar 

  13. Straube A, Aicher B, Förderreuther S, Eggert T, Köppel J, Möller S et al (2013) Period prevalence of self-reported headache in the general population in Germany from 1995-2005 and 2009: results from annual nationwide population-based cross-sectional surveys. J Headache Pain. 14:11

    Article  PubMed  PubMed Central  Google Scholar 

  14. Ayzenberg I, Katsarava Z, Sborowski A, Chernysh M, Osipova V, Tabeeva G et al (2012) The prevalence of primary headache disorders in Russia: a countrywide survey. Cephalalgia. 32:373–381

    Article  CAS  PubMed  Google Scholar 

  15. Stovner LJ, Andree C (2010) Prevalence of headache in Europe: a review for the Eurolight project. J Headache Pain. 11:289–299

    Article  PubMed  PubMed Central  Google Scholar 

  16. Linde M, Stovner LJ, Zwart JA, Hagen K (2011) Time trends in the prevalence of headache disorders. The Nord-Trondelag health studies (HUNT 2 and HUNT 3). Cephalalgia. 31:585–596

    Article  PubMed  Google Scholar 

  17. Lj S, Hagen K, Jensen R, Katsarava Z, Lipton R, Scher A et al (2007) The global burden of headache: a documentation of headache prevalence and disability worldwide. Cephalalgia. 27:193–210

    Article  Google Scholar 

  18. Nahin RL, Sayer B, Stussman BJ, Feinberg TM (2019) Eighteen-year trends in the prevalence of, and health care use for, noncancer pain in the United States: data from the medical expenditure panel survey. J Pain 20:796–809

    Article  PubMed  Google Scholar 

  19. Harrison JM, Lagisetty P, Sites BD, Guo C, Davis MA (2018) Trends in prescription pain medication use by race/ethnicity among US adults with noncancer pain, 2000-2015. Am J Public Health 108(6):788–790

    Article  PubMed  Google Scholar 

  20. Luo X, Pietrobon R, Hey L (2004) Patterns and trends in opioid use among individuals with back pain in the United States. Spine (Phila Pa 1976) 29:884–890

    Article  Google Scholar 

  21. Ropponen A, Narusyte J, Mittendorfer-Rutz E, Svedberg P (2019) Number of pain locations as predictor of cause-specific disability pension in Sweden- do common mental disorders play a role? J Occup Environ Med 61:646–652

    Article  PubMed  Google Scholar 

  22. Porter JK, Di Tanna GL, Lipton RB, Sapra S, Villa G (2019) Costs of acute headache medication use and productivity losses among patients with migraine: insights from three randomized controlled trials. Pharmacoecon Open 3:411–417

    Article  PubMed  Google Scholar 

  23. Raggi A, Corso B, De Torres L, Quintas R, Chatterji S, Sainio P et al (2018) Determinants of mobility in populations of older adults: results from a cross-sectional study in Finland. Poland and Spain Maturitas 115:84–91

    PubMed  Google Scholar 

  24. Raggi A, Corso B, Minicuci N, Quintas R, Sattin D, De Torres L et al (2016) Determinants of quality of life in ageing populations: results from a cross-sectional study in Finland. Poland and Spain PLoS One 11:e0159293

    Article  PubMed  CAS  Google Scholar 

  25. de Sola H, Salazar A, Dueñas M, Ojeda B, Failde I (2016) Nationwide cross-sectional study of the impact of chronic pain on an individual's employment: relationship with the family and the social support. BMJ Open 6:e012246

    Article  PubMed  PubMed Central  Google Scholar 

  26. Dorner TE, Alexanderson K, Svedberg P, Ropponen A, Stein KV, Mittendorfer-Rutz E (2015) Sickness absence due to back pain or depressive episode and the risk of all-cause and diagnosis-specific disability pension: a Swedish cohort study of 4,823,069 individuals. Eur J Pain 19:1308–1320

    Article  CAS  PubMed  Google Scholar 

  27. Selekler HM, Gökmen G, Alvur TM, Steiner TJ (2015) Productivity losses attributable to headache, and their attempted recovery, in a heavy-manufacturing workforce in Turkey: implications for employers and politicians. J Headache Pain. 16:96

    Article  PubMed  PubMed Central  Google Scholar 

  28. Natvig B, Eriksen W, Bruusgaard D (2002) Low back pain as a predictor of long-term work disability. Scand J Public Health 30:288–292

    Article  PubMed  Google Scholar 

  29. Raggi A, Leonardi M, Sansone E, Curone M, Grazzi L, D'Amico D (2020) The cost and the value of treatment of medication overuse headache in Italy: a longitudinal study based on patient-derived data. Eur J Neurol 27:62–e1

    Article  CAS  PubMed  Google Scholar 

  30. Messali A, Sanderson JC, Blumenfeld AM, Goadsby PJ, Buse DC, Varon SF et al (2016) Direct and indirect costs of chronic and episodic migraine in the United States: a web-based survey. Headache 56:306–322

    Article  PubMed  Google Scholar 

  31. Gustavsson A, Bjorkman J, Ljungcrantz C, Rhodin A, Rivano-Fischer M, Sjolund KF et al (2012) Socio-economic burden of patients with a diagnosis related to chronic pain--register data of 840,000 Swedish patients. Eur J Pain 16:289–299

    Article  CAS  PubMed  Google Scholar 

  32. Linde M, Gustavsson A, Stovner LJ, Steiner TJ, Barré J, Katsarava Z et al (2012) The cost of headache disorders in Europe: the Eurolight project. Eur J Neurol 19:703–711

    Article  CAS  PubMed  Google Scholar 

  33. Juniper M, Le TK, Mladsi D (2009) The epidemiology, economic burden, and pharmacological treatment of chronic low back pain in France, Germany, Italy, Spain and the UK: a literature-based review. Expert Opin Pharmacother 10:2581–2592

    Article  CAS  PubMed  Google Scholar 

  34. Sanchez-Niubo A, Egea-Cortés L, Olaya B, Caballero FF, Ayuso-Mateos JL, Prina M et al (2019) Cohort profile: The Ageing Trajectories of Health – Longitudinal Opportunities and Synergies (ATHLOS) project. Int J Epidemiol 48:1052–1053i

  35. Leonardi M, Chatterji S, Koskinen S, Ayuso-Mateos JL, Haro JM, Frisoni G et al (2014) Determinants of health and disability in ageing population: the COURAGE in Europe project (collaborative research on ageing in Europe). Clin Psychol Psychother 21:193–198

    Article  PubMed  Google Scholar 

  36. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G (2014) Cohort profile: the China health and retirement longitudinal study (CHARLS). Int J Epidemiol 43:61–68

    Article  PubMed  Google Scholar 

  37. Luszcz MA, Giles LC, Anstey KJ, Browne-Yung KC, Walker RA, Windsor TD (2016) Cohort profile: the Australian longitudinal study of ageing (ALSA). Int J Epidemiol 45:1054–1063

    Article  PubMed  Google Scholar 

  38. Börsch-Supan A, Brandt M, Hunkler C, Kneip T, Korbmacher J, Malter F et al (2013) Data resource profile: the survey of health, ageing and retirement in Europe (SHARE). Int J Epidemiol 42:992–1001

    Article  PubMed  PubMed Central  Google Scholar 

  39. Whelan BJ, Savva GM (2013) Design and methodology of the Irish longitudinal study on ageing. J Am Geriatr Soc 61(Suppl 2):S265–S268

    Article  PubMed  Google Scholar 

  40. Riebler A, Held L, Rue H (2012) Estimation and extrapolation of time trends in registry data—borrowing strength from related populations. Ann Appl Stat 6:304–333

    Article  Google Scholar 

  41. Smith TR, Wakefield J (2016) A review and comparison of age–period–cohort models for cancer incidence. Stat Sci 31:591–610

    Article  Google Scholar 

  42. Holford TR (1983) The estimation of age, period and cohort effects for vital rates. Biometrics 39:311–324

    Article  CAS  PubMed  Google Scholar 

  43. Holford T (2005) Age-period-cohort analysis. In: Armitage P, Colton T (eds) Encyclopaedia of biostatistics, 2nd edn. Wiley, West Sussex, pp 105–123

    Google Scholar 

  44. Heuer C (1997) Modeling of time trends and interactions in vital rates using restricted regression splines. Biometrics 53:161–177

    Article  CAS  PubMed  Google Scholar 

  45. Clayton D, Schifflers E (1987) Models for temporal variation in cancer rates. II: age-period-cohort models. Stat Med 6:469–481

    Article  CAS  PubMed  Google Scholar 

  46. Rue H, Martino S, Chopin N (2009) Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J R Stat Soc Ser B Stat Methodol 71:319–392

    Article  Google Scholar 

  47. Eilstein D, Uhry Z, Lim TA, Bloch J (2008) Lung Cancer mortality in France. Trend analysis and projection between 1975 and 2012, using a Bayesian age-period-cohort model. Lung Cancer 59:282–290

    Article  PubMed  Google Scholar 

  48. Knorr-Held L, Rainer E (2001) Projections of lung cancer mortality in West Germany: a case study in Bayesian prediction. Biostatistics 2:109–129

    Article  CAS  PubMed  Google Scholar 

  49. Girardi P, Bressan V, Merler E (2014) Past trends and future prediction of mesothelioma incidence in an industrialized area of Italy, the Veneto region. Cancer Epidemiol 38:496–503

    Article  PubMed  Google Scholar 

  50. Riebler A, Held L (2017) Projecting the future burden of cancer: Bayesian age-period-cohort analysis with integrated nested Laplace approximations. Biom J 59:531–549

    Article  PubMed  Google Scholar 

  51. Spiegelhalter D, Best N, Carlin B, Van Der Linde A (2002) Bayesian measures of model complexity and fit. J R Stat Soc Ser B Stat Methodol 64:583–616

    Article  Google Scholar 

  52. Besag J, Green P, Higdon D, Mengersen K (1995) Bayesian computation and stochastic systems (with discussion). Stat Sci 10:3–66

    Article  Google Scholar 

  53. Rue H, Held L (2005) Gaussian Markov random fields, vol 104. Chapman & Hall/CRC Press, London

    Book  Google Scholar 

  54. R Core Team (2018) R. A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna https://www.R-project.org/ (accessed 10 Feb 2020)

    Google Scholar 

  55. Martins TG, Simpson D, Lindgren F, Rue H (2013) Bayesian computing with INLA: new features. Comput Stat Data Analysis 67:68–83

    Article  Google Scholar 

  56. Lindgren F, Rue H, Lindstrom J (2011) An explicit link between gaussian fields and gaussian markov random fields: the stochastic partial differential equation. J R Stat Soc Ser B Stat Methodol 73:423–498

    Article  Google Scholar 

  57. Rue H, Riebler A, Sorbye SH, Illian JB, Simpson DP, Lindgren FK (2017) Bayesian computing with INLA: a review. Annu Rev Stat Its Appl 4:395–421

    Article  Google Scholar 

  58. Jiménez-Sánchez S, Jiménez-García R, Hernández-Barrera V, Villanueva-Martínez M, Ríos-Luna A, Fernández-de-las-Peñas C (2010) Has the prevalence of invalidating musculoskeletal pain changed over the last 15 years (1993-2006)? A Spanish population-based survey. J Pain 11(7):612–620

    Article  PubMed  Google Scholar 

  59. Celeste RK, Fritzell J (2018) Do socioeconomic inequalities in pain, psychological distress and oral health increase or decrease over the life course? Evidence from Sweden over 43 years of follow-up. J Epidemiol Community Health 72:160–167

    Article  PubMed  Google Scholar 

  60. Tesfaye S (2011) Recent advances in the management of diabetic distal symmetrical polyneuropathy. J Diabetes Investig 2:33–42

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Danaei G, Finucane MM, Lu Y, Singh GM, Cowan MJ, Paciorek CJ et al (2011) National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2·7 million participants. Lancet. 378:31–40

    Article  CAS  PubMed  Google Scholar 

  62. NCD Risk Factor Collaboration (NCD-RisC) (2016) Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet 387:1513–1530

    Article  Google Scholar 

  63. Korolainen MA, Kurki S, Lassenius MI, Toppila I, Costa-Scharplatz M, Purmonen T et al (2019) Burden of migraine in Finland: health care resource use, sick-leaves and comorbidities in occupational health care. J Headache Pain. 20(1):13

    Article  PubMed  PubMed Central  Google Scholar 

  64. Takeshima T, Wan Q, Zhang Y, Komori M, Stretton S, Rajan N et al (2019) Prevalence, burden, and clinical management of migraine in China, Japan, and South Korea: a comprehensive review of the literature. J Headache Pain 20(1):111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Straube A, Andreou A (2019) Primary headaches during lifespan. J Headache Pain. 20(1):35

    Article  PubMed  PubMed Central  Google Scholar 

  66. Woldeamanuel YW, Cowan RP (2017) Migraine affects 1 in 10 people worldwide featuring recent rise: a systematic review and meta-analysis of community-based studies involving 6 million participants. J Neurol Sci 372:307–315

    Article  PubMed  Google Scholar 

  67. Vetvik KG, Macgregor EA, Lundqvist C, Russell MB (2014) Prevalence of menstrual migraine: a population-based study. Cephalalgia. 34(4):280–288

    Article  PubMed  Google Scholar 

  68. Vetvik KG, MacGregor EA, Lundqvist C, Russell MB (2018) Symptoms of premenstrual syndrome in female migraineurs with and without menstrual migraine. J Headache Pain. 19(1):97

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. United Nations (2017) Department of Economic and Social Affairs, population division. World population prospects: the 2017 revision, volume II: demographic profiles (ST/ESA/SER.A/400). United Nations, New York

    Google Scholar 

  70. Vivekanantham A, Edwin C, Pincus T, Matharu M, Parsons H, Underwood M (2019) The association between headache and low back pain: a systematic review. J Headache Pain. 20(1):82

    Article  PubMed  PubMed Central  Google Scholar 

  71. National Osteoporosis Foundation (2002) America’s Bone Health: The State of Osteoporosis and Low Bone Mass in Our Nation. National Osteoporosis Foundation, Washington, DC

    Google Scholar 

  72. Nguyen ND, Pongchaiyakul C, Center JR, Eisman JA, Nguyen TV (2005) Identification of high-risk individuals for hip fracture: a 14-year prospective study. J Bone Miner Res 20:1921–1928

    Article  PubMed  Google Scholar 

  73. Witt EA, Kenworthy J, Isherwood G, Dunlop WC (2016) Examining the association between pain severity and quality-of-life, work-productivity loss, and healthcare resource use among European adults diagnosed with pain. J Med Econ 19:858–865

    Article  PubMed  Google Scholar 

  74. Müller-Schwefe G, Freytag A, Höer A, Schiffhorst G, Becker A, Casser HR et al (2011) Healthcare utilization of back pain patients: results of a claims data analysis. J Med Econ 14:816–823

    Article  PubMed  Google Scholar 

  75. Grazzi L, Grignani E, D’Amico D, Sansone E, Raggi A (2018) Is medication overuse drug specific or not? Data from a review of published literature and from an original study on Italian MOH patients. Curr Pain Headache Rep 22:71

    Article  PubMed  Google Scholar 

  76. Coutinho AD, Gandhi K, Fuldeore RM, Landsman-Blumberg PB, Gandhi S (2018) Long-term opioid users with chronic noncancer pain: assessment of opioid abuse risk and relationship with healthcare resource use. J Opioid Manag 14:131–141

    Article  PubMed  Google Scholar 

  77. Morasco BJ, Yarborough BJ, Smith NX, Dobscha SK, Deyo RA, Perrin NA et al (2017) Higher prescription opioid dose is associated with worse patient-reported pain outcomes and more health care utilization. J Pain 18:437–445

    Article  PubMed  Google Scholar 

  78. Agaliotis M, Mackey MG, Jan S, Fransen M (2014) Burden of reduced work productivity among people with chronic knee pain: a systematic review. Occup Environ Med 71:651–659

    Article  PubMed  Google Scholar 

  79. Kristoffersen ES, Stavem K, Lundqvist C, Russell MB (2019) Impact of chronic headache on workdays, unemployment and disutility in the general population. J Epidemiol Community Health 73:360–367

    Article  PubMed  Google Scholar 

  80. Leonardi M, Raggi A (2019) A narrative review on the burden of migraine: when the burden is the impact on people's life. J Headache Pain. 20:41

    Article  PubMed  PubMed Central  Google Scholar 

  81. Vo P, Fang J, Bilitou A, Laflamme AK, Gupta S (2018) Patients' perspective on the burden of migraine in Europe: a cross-sectional analysis of survey data in France, Germany, Italy, Spain, and the United Kingdom. J Headache Pain. 19:82

    Article  PubMed  PubMed Central  Google Scholar 

  82. Neupane S, Nygård CH, Prakash KC, von Bonsdorff MB, von Bonsdorff ME, Seitsamo J et al (2018) Multisite musculoskeletal pain trajectories from midlife to old age: a 28-year follow-up of municipal employees. Occup Environ Med 75:863–870

    Article  PubMed  Google Scholar 

  83. Laires PA, Canhão H, Rodrigues AM, Eusébio M, Gouveia M, Branco JC (2018) The impact of osteoarthritis on early exit from work: results from a population-based study. BMC Public Health 18:472

    Article  PubMed  PubMed Central  Google Scholar 

  84. Keysor JJ, LaValley MP, Brown C, Felson DT, AlHeresh RA, Vaughan MW et al (2018) Efficacy of a Work Disability Prevention Program for People with Rheumatic and Musculoskeletal Conditions: A Single-Blind Parallel-Arm Randomized Controlled Trial. Arthritis Care Res (Hoboken) 70:1022–1029

    Article  Google Scholar 

  85. Laires PA, Gouveia M, Canhão H (2017) Interventions aiming to reduce early retirement due to rheumatic diseases. Acta Reumatol Port 42:240–248

    PubMed  Google Scholar 

  86. Oakman J, Kinsman N, Briggs AM (2017) Working with persistent pain: an exploration of strategies utilised to stay productive at work. J Occup Rehabil 27:4–14

    Article  PubMed  Google Scholar 

  87. Vicente-Herrero T, Burke TA, Laínez MJ (2004) The impact of a worksite migraine intervention program on work productivity, productivity costs, and non-workplace impairment among Spanish postal service employees from an employer perspective. Curr Med Res Opin 20:1805–1814

    Article  PubMed  Google Scholar 

  88. Rota E, Evangelista A, Ceccarelli M, Ferrero L, Milani C, Ugolini A et al (2016) Efficacy of a workplace relaxation exercise program on muscle tenderness in a working community with headache and neck pain: a longitudinal, controlled study. Eur J Phys Rehabil Med 52:457–465

    PubMed  Google Scholar 

  89. Parker C, Waltman N (2012) Reducing the frequency and severity of migraine headaches in the workplace: implementing evidence-based interventions. Workplace Health Saf 60:12–18

    PubMed  Google Scholar 

  90. Barton GR, Sach TH, Jenkinson C, Doherty M, Avery AJ, Muir KR (2009) Lifestyle interventions for knee pain in overweight and obese adults aged > or = 45: economic evaluation of randomised controlled trial. BMJ 339:b2273

    Article  PubMed  PubMed Central  Google Scholar 

  91. Hagen K, Åsberg AN, Stovner L, Linde M, Zwart JA, Winsvold BS et al (2018) Lifestyle factors and risk of migraine and tension-type headache. Follow-up data from the Nord-Trøndelag health surveys 1995-1997 and 2006-2008. Cephalalgia. 38:1919–1926

    Article  PubMed  Google Scholar 

  92. Verrotti A, Di Fonzo A, Penta L, Agostinelli S, Parisi P (2014) Obesity and headache/migraine: the importance of weight reduction through lifestyle modifications. Biomed Res Int 2014:420858

    Article  PubMed  PubMed Central  Google Scholar 

  93. Majeed MH, Sudak DM (2017) Cognitive behavioral therapy for chronic pain-one therapeutic approach for the opioid epidemic. J Psychiatr Pract 23:409–414

    Article  PubMed  Google Scholar 

  94. Van Denburg AN, Vilardaga JP, Shelby RA, Keefe FJ (2018) Opioid therapy and persistent pain: can cognitive behavioral therapy help? Pain. 159:411–415

    Article  PubMed  Google Scholar 

  95. Castelnuovo G, Giusti EM, Manzoni GM, Saviola D, Gatti A, Gabrielli S et al (2016) Psychological Treatments and Psychotherapies in the Neurorehabilitation of Pain: Evidences and Recommendations from the Italian Consensus Conference on Pain in Neurorehabilitation. Front Psychol 7:115

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The ATHLOS project researchers are grateful for data contribution and funding in the following studies:

- The 10/66 study (10/66): The 10/66 study is supported by the Wellcome Trust (GR066133/ GR080002), the European Research Council (340755), US Alzheimer’s Association, WHO, FONDACIT (Venezuela) and the Puerto Rico State Government, and the Medical Research Council (MR/K021907/1 to A.M.P.). The authors gratefully acknowledge the work of the 10/66 Dementia Research Group who provided data for this paper.

- The Australian Longitudinal Study of Ageing (ALSA): The ALSA study was supported by grants from the South Australian Health Commission, the Australian Rotary Health Research Fund, the US National Institute on Aging (Grant No. AG 08523–02), the Office for the Ageing (SA), Elderly Citizens Homes (SA), the National Health and Medical Research Council (NH&MRC 22922), the Premiers Science Research Fund (SA) and the Australian Research Council (DP0879152; DP130100428). The authors gratefully acknowledge the work of the project team at the Flinders Centre for Ageing Studies, Flinders University who provided data for this paper.

- The China Health and Retirement Longitudinal Study (CHARLS): The CHARLS study has received critical support from Peking University, the National Natural Science Foundation of China, the Behavioral and Social Research Division of the National Institute on Aging and the World Bank. The authors gratefully acknowledge the work of the project team at the Peking University who provided data for this paper.

- Collaborative Research on Ageing (COURAGE) in Europe: The COURAGE study was supported by the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement number 223071 (COURAGE in Europe). Data from Spain were also collected with support from the Instituto de Salud Carlos III-FIS research grants number PS09/00295, PS09/01845, PI12/01490, PI13/00059, PI16/00218 and PI16/01073; the Spanish Ministry of Science and Innovation ACI-Promociona (ACI2009-1010); the European Regional Development Fund (ERDF) ‘A Way to Build Europe’ grant numbers PI12/01490 and PI13/00059; and by the Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III. Data from Poland were collected with support from the Polish Ministry for Science and Higher Education grant for an international co-financed project (number 1277/7PR/UE/2009/7, 2009–2012) and Jagiellonian University Medical College grant for project COURAGE-POLFUS (K/ZDS/005241). The authors gratefully acknowledge the work of COURAGE researchers who provided data for this paper.

- The English Longitudinal Study of Ageing (ELSA): ELSA is supported by the U.S. National Institute of Aging, the National Centre for Social Research, the University College London (UCL) and the Institute for Fiscal Studies. The authors gratefully acknowledge the UK Data Service and UCL who provided data for this paper.

- The Seniors-ENRICA: The Seniors-ENRICA cohort was funded by an unconditional grant from Sanofi-Aventis, the Ministry of Health of Spain, FIS grant 12/1166 (State Secretary for R + D and FEDER-FSE) and the Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III. The authors gratefully acknowledge the work of the project team at the Universidad Autónoma de Madrid who provided data for this paper.

- The Health, Alcohol and Psychosocial factors In Eastern Europe (HAPIEE) study: The HAPIEE study was supported by the Wellcome Trust [grant numbers WT064947, WT081081], the US National Institute of Aging [grant number 1RO1AG23522] and the MacArthur Foundation Initiative on Social Upheaval and Health. The authors gratefully acknowledge the work of the project teams at University College London, the National Institute of Public Health in Prague, the Jagiellonian University Medical College in Krakow and the Kaunas University of Medicine who provided data for this paper.

- The Health 2000/2011 study: The authors gratefully acknowledge the Finnish Institute for Health and Welfare who provided data for this paper.

- Health and Retirement Study (HRS): The HRS study is supported by the National Institute on Aging (grant number NIA U01AG009740) and the Social Security Administration, and is conducted by the University of Michigan. The authors gratefully acknowledge the University of Michigan who provided data for this paper.

- The Korean Longitudinal Study of Ageing (KLOSA): The KLOSA study is funded by the Korea Employment Information Service (KEIS) and was supported by the Korea Labor Institute’s KLOSA Team. The authors gratefully acknowledge the KEIS who provided data for this paper.

- The Mexican Health and Aging Study (MHAS): The MHAS study is partly sponsored by the National Institutes of Health/National Institute on Aging (grant number NIH R01AG018016) and the INEGI in Mexico. The authors gratefully acknowledge the MHAS team who provided data for this paper retrieved from www.MHASweb.org

- The Study on Global Ageing and Adult Health (SAGE): The SAGE study is funded by the U.S. National Institute on Aging and has received financial support through Interagency Agreements (OGHA 04034785; YA1323-08-CN-0020; Y1-AG-1005–01) and Grants (R01-AG034479; IR21-AG034263-0182). The authors gratefully acknowledge the World Health Organization who provided data for this paper.

- The Survey of Health, Ageing and Retirement in Europe (SHARE): The SHARE study is funded by the European Commission through FP5 (QLK6-CT-2001–00360), FP6 (SHARE-I3: RII-CT-2006–062193, COMPARE: CIT5-CT-2005–028857, SHARELIFE: CIT4-CT-2006–028812) and FP7 (SHARE-PREP: N°211909, SHARE-LEAP: N°227822, SHARE M4: N°261982). Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the U.S. National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553–01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C) and from various national funding sources is gratefully acknowledged (see www.share-project.org).

- The Irish Longitudinal study on Ageing (TILDA): The authors gratefully acknowledge the Trinity College Dublin and the Irish Social Science Data Archive (www.ucd.ie/issda) who provided data for this paper.

Alberto Raggi is supported by a grant from the Italian Ministry of Health (Ricerca Corrente, Fondazione Istituto Neurologico C. Besta, Linea 4—Outcome Research: dagli Indicatori alle Raccomandazioni Cliniche).

Stefanos Tyrovolas was supported by the Foundation for Education and European Culture, the Miguel Servet programme (reference CP18/00006), and the Fondos Europeos de Desarrollo Regional.

Funding

The ATHLOS project (Ageing Trajectories of Health: Longitudinal Opportunities and Synergies) has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 635316. The funding body had no role in the design of the study, analysis and interpretation of data, and in writing the manuscript.

Author information

Authors and Affiliations

  1. Neurology, Public Health and Disability Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy

    Davide Guido, Matilde Leonardi & Alberto Raggi

  2. Parc Sanitari Sant Joan de Déu, Fundacion Sant Joan de Deu, Barcelona, Spain

    Blanca Mellor-Marsá, Maria V. Moneta, Albert Sanchez-Niubo, Stefanos Tyrovolas, Iago Giné-Vázquez & Josep M. Haro

  3. Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain

    Albert Sanchez-Niubo, Stefanos Tyrovolas, Iago Giné-Vázquez, Josep M. Haro & Jose L. Ayuso-Mateos

  4. Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece

    Stefanos Tyrovolas & Demosthenes Panagiotakos

  5. Information, Evidence and Research, World Health Organization, Geneva, Switzerland

    Somnath Chatterji

  6. Research Department of Epidemiology and Public Health, University College London, London, UK

    Martin Bobak

  7. Department of Psychiatry, Universidad Autónoma de Madrid, Madrid, Spain

    Jose L. Ayuso-Mateos

  8. Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria Princesa, Madrid, Spain

    Jose L. Ayuso-Mateos

  9. SPRING TECHNO GMBH & Co. KG, Bremen, Germany

    Holger Arndt

  10. Department of Public Health Sciences, Centre for Health Equity Studies, Stockholm University, Stockholm, Sweden

    Ilona Koupil

  11. Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden

    Ilona Koupil

  12. Department of Health Sciences and Health Policy, University of Lucerne, Lucerne, Switzerland

    Jerome Bickenbach

  13. Swiss Paraplegic Research, Nottwil, Switzerland

    Jerome Bickenbach

  14. Finnish Institute for Health and Welfare (THL), Helsinki, Finland

    Seppo Koskinen

  15. Department of Epidemiology and Population Studies, Faculty of Health Sciences, Jagiellonian University Medical College, Krakow, Poland

    Beata Tobiasz-Adamczyk

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  1. Davide Guido
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Contributions

DG, planned and ran the analyses; ML, planned the analyses and revised the manuscript for intellectual content; BMM, MVM, ASN, ST, IGV, JMH, SC, MB, JLAM, HA, IK, JB, SK, BTA and DP revised the manuscript for intellectual content; AR, planned the analyses, drafted the manuscript and revised it for intellectual content. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Matilde Leonardi.

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Not applicable for ATHLOS study: ethics approval and participants’ consent to participate were obtained by each study at the time point of surveys’ field trials.

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Not applicable.

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The authors declare that they have no competing interests.

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Supplementary information

Additional file 1.

Trends Pain – Supplementary Materials – February 10, 2020. supplementary results not included in the full text.

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Guido, D., Leonardi, M., Mellor-Marsá, B. et al. Pain rates in general population for the period 1991–2015 and 10-years prediction: results from a multi-continent age-period-cohort analysis. J Headache Pain 21, 52 (2020). https://doi.org/10.1186/s10194-020-01108-3

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Keywords

The Journal of Headache and Pain

ISSN: 1129-2377

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