Raggi et al. The Journal of Headache and Pain (2020) 21:45 https://doi.org/10.1186/s10194-020-01116-3 The Journal of Headache and Pain RESEARCH ARTICLE Open Access Predictors of pain in general ageing populations: results from a multi-country analysis based on ATHLOS harmonized database Alberto Raggi1, Matilde Leonardi1* , Blanca Mellor-Marsá2, Maria V. Moneta2, Albert Sanchez-Niubo2,3, Stefanos Tyrovolas2,3,4, Iago Giné-Vázquez2,3, Josep M. Haro2,3, Somnath Chatterji5, Martin Bobak6, Jose L. Ayuso-Mateos3,7,8, Holger Arndt9, Muhammad Z. Hossin10, Jerome Bickenbach11,12, Seppo Koskinen13, Beata Tobiasz-Adamczyk14, Demosthenes Panagiotakos4 and Barbara Corso15 Abstract Background: Pain is a common symptom, often associated with neurological and musculoskeletal conditions, and experienced especially by females and by older people, and with increasing trends in general populations. Different risk factors for pain have been identified, but generally from studies with limited samples and a limited number of candidate predictors. The aim of this study is to evaluate the predictors of pain from a large set of variables and respondents. Methods: We used part of the harmonized dataset of ATHLOS project, selecting studies and waves with a longitudinal course, and in which pain was absent at baseline and with no missing at follow-up. Predictors were selected based on missing distribution and univariable association with pain, and were selected from the following domains: Socio- demographic and economic characteristics, Lifestyle and health behaviours, Health status and functional limitations, Diseases, Physical measures, Cognition, personality and other psychological measures, and Social environment. Hierarchical logistic regression models were then applied to identify significant predictors. Results: A total of 13,545 subjects were included of whom 5348 (39.5%) developed pain between baseline and the average 5.2 years’ follow-up. Baseline risk factors for pain were female gender (OR 1.34), engaging in vigorous exercise (OR 2.51), being obese (OR 1.36) and suffering from the loss of a close person (OR 1.88) whereas follow-up risk factors were low energy levels/fatigue (1.93), difficulties with walking (1.69), self-rated health referred as poor (OR 2.20) or average to moderate (OR 1.57) and presence of sleep problems (1.80). (Continued on next page) * Correspondence: matilde.leonardi@istituto-besta.it 1 Neurology, Public Health and Disability Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133 Milan, Italy Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Raggi et al. The Journal of Headache and Pain (2020) 21:45 Page 2 of 12 (Continued from previous page) Conclusions: Our results showed that 39.5% of respondents developed pain over a five-year follow-up period, that there are proximal and distal risk factors for pain, and that part of them are directly modifiable. Actions aimed at improving sleep, reducing weight among obese people and treating fatigue would positively impact on pain onset, and avoiding vigorous exercise should be advised to people aged 60 or over, in particular if female or obese. Keywords: Pain, Risk factors, Headache disorders, Musculoskeletal disorders, Sleep, Obesity, Exercise, Bereavement, Fatigue, Walking Introduction regional pain syndrome type 1 [30]. Evidence was also Pain is one of the most common symptoms in general found on the impact of some mental health problems populations and is the core symptom of many common, and symptoms on pain rates and severity, including sleep and often comorbid, clinical conditions. In fact, the preva- problems [6, 27, 34, 38, 39], depressed mood or anxiety lence of pain-associated conditions is between 10% and [26, 28–30, 34, 35, 39, 40], cognitive complaints [34] and 60% approximately, with the highest rates being observed fatigue or lack of energy [38]. Other health status vari- for tension-type headache [1–11]. Data referred to general ables that were found to be associated with higher pain populations are on the contrary almost lacking. A recent rates and severity included the presence of comorbidities study from the U.S. showed that the overall rates of non- and multimorbidity status [6, 26, 30, 35, 39, 41] and, cancer pain prevalence increased from 32.9% to 41.0% among single heath conditions, diabetes and stroke [30, over an 18-years period (1997–98 to 2013–14) [12]. Such 42, 43]: of course, presence of conditions whose cardinal an information was confirmed in a recent study based on symptom is pain, such as headaches, musculoskeletal the harmonized dataset generated by ATHLOS project conditions or angina, is clearly acknowledged to impact (Ageing Trajectories of Health – Longitudinal Opportun- on pain. Finally, a set of risk and protective lifestyle fac- ities and Synergies) [13]. Such a recent paper showed that tors were also identified. Physical activity was found to pain rates largely vary by sex, participants’ age, period of be protective against back pain onset among older adult inclusion in the studies and birth cohort [14]. In particu- women [29], smoking was found to be a risk factor for lar, pain rates were shown to vary between 20 and 30% in the development of chronic musculoskeletal pain [44], males enrolled in the early 1990s, and 60–70% among and high body mass index (BMI) or obesity status were old-age women enrolled between 2011 and 2015. In risk factors for higher pain rates, development of muscu- addition to this, the 10-years forecast suggested an in- loskeletal conditions and pain worsening [6, 27, 29, 30, crease in the trends of pain which will peak up to 20% 33, 35, 45, 46]. among females and among older subjects [14]. The information herein available is however limited to Given the observed and predicted increase of pain specific cohorts, such as military or farmers, or cohort of rates, determinants need to be addressed in order to patients, with population studies presenting a limited limit to the widest extent the possible negative conse- amount of pain predictors, a relatively limited amount of quences of such an expansion, for example the risk of participants, with different ages and a limited geograph- future opioids and other drugs’ overuse, increase in dis- ical distribution. This provides a partial appreciation of ability, reduction in quality of life and employment rates, global pain predictors, which can be on the contrary and productivity loss [15–25]. A large amount of studies achieved through an analysis of a large dataset such as identified potential predictors of pain, both in clinical that of ATHLOS project, where data from 17 different trials and in population or cohort studies. Among popu- population survey conducted in the five continents were lation and cohort studies, evidence existed on the impact harmonized [13]. This paper aims to provide the most of sociodemographic characteristics, with females [26– comprehensive identification of pain predictors in ageing 33] and older people [6, 27, 28, 31, 32, 34] reporting populations. higher pain rates or severity. Also, people with lower education level and those unemployed had higher pain Methods rates [35, 36], but contrasting results were found with Study population reference to socioeconomic position: in fact, Chen and The last available wave of each study (i.e. harmonized in colleagues found that people with a lower socioeconomic the ATHLOS project) was defined as follow-up wave, class had a more severe trajectory with regard to low while the baseline wave was selected trying to keep, back pain severity and persistence [37], whereas Elshary- when possible, one wave in between the two. The base- dah and colleagues found that higher median household line wave did not necessarily coincide with study’s base- income was associated with higher rate of complex line wave. Raggi et al. The Journal of Headache and Pain (2020) 21:45 Page 3 of 12 The studies and waves selected for the present ana- Next, two stepwise forward logistic regressions were lyses were: the China Health and Retirement Longitu- performed (the p-value for variable inclusion and re- dinal Study (CHARLS: W1-W2), the Collaborative moval were set to p < 0.05 and p < 0.15, respectively). Research on Ageing in Europe (COURAGE in Europe: The first stepwise was performed with baseline variables W1-W2), the Health and Retirement Study (HRS: W9- only as predictors of pain development at follow-up, and W11), the Health 2000/2011 study (W1-W2), the Mexi- later a multivariable logistic regression was implemented can Health and Aging Study (MHAS: W2-W3), and the retaining only statistically significant predictors (p < Survey of Health, Ageing and Retirement in Europe 0.05). Then, in the second stepwise, the significant pre- (SHARE: W4-W5). On average, the follow-up waves dictors identified at baseline were kept in the model and were carried out 5.2 (SD 3.2) years after the baseline follow-up variables were included for selection. Lastly, a waves (see Table S1 in supplementary materials for pain final model was pursued with only significant predictors variable distribution and follow-up duration in the dif- at both baseline and follow-up. All models were ferent studies). weighted and adjusted for a time-lag variable (defined as the difference between the year of interview at follow-up Variables of interest and baseline). Multicollinearity was checked using toler- The outcome variable was the absence/presence of the ance and Variance Inflation Factor (VIF): variables with harmonized pain variable (defined as “self-reported pain tolerance < 0.4 (VIF > 2.5) were discarded from the ana- experienced at the time of the interview”) at follow-up. lysis. Engagement in vigorous exercise at follow-up was Absence of pain was defined as absence of pain at both discarded form the analyses due to collinearity. Models’ baseline and follow-up wave, while presence of pain was goodness-of-fit were assessed by the Hosmer and Leme- identified as absence of pain at baseline but presence of show’s test and by visual inspection of the plot of esti- pain at follow-up. In total, 91,278 subjects did not report mated values against residuals, whilst specification error pain at baseline and, of them, 50,849 had complete data was evaluated by running a new regression with the on pain at follow-up: of them 36,023 still did not report observed values against predicted and predicted-squared pain, whereas 14,826 (29.2%) reported pain. values as independent variables. The Area Under the Re- Candidate predictors of pain were selected from the dif- ceiver Operating Curve (AUC) and its 95% confidence ferent domains defined in the ATHLOS harmonization. interval (95% CI) for the predicted versus the actual data Domain “Lifestyle and health behaviours”: current smok- was also calculated. Categorical variables were reported ing status, current alcohol use and engagement in vigor- as proportions, and continuous variables were reported ous exercise; “Health status and functional limitations”: as means ± standard deviations (SD) and medians with level of energy, sleep quality, mobility walk, self-reported interquartile ranges (IQRs). Odds ratios (ORs) were pre- health, presence of recent falls and evaluative wellbeing; sented with their 95% CI and Z with its associated p- “Diseases”: diabetes, respiratory disease, hypertension, value. All statistical analyses were performed using joint disorders, angina, stroke, cancer and multimorbidity; STATA SE, version 15.0 (Stata Statistical Software: Re- “Physical measures”: obesity; “Cognition, personality and lease 12. College Station, TX: StataCorp LP). other psychological measures”: depression; “Social envir- onment”: bereavement, i.e. the experience of a loss of any Results close person. Moreover, in the domain “Socio-demo- Almost all variables, at both baseline and follow-up, graphic and economic characteristics”, gender, marital sta- were significant predictors of pain in univariable regres- tus, education, retirement status and wealth quintile of sion models (see Table S2 in supplementary materials): participants were retrieved. All these variables were se- the exceptions to this were being single (vs. married) at lected from both baseline and follow-up waves (see Table baseline and being retired (vs. employed or student) at S2 in supplementary materials for the definition of harmo- follow-up. nized variables). Stepwise regression with baseline variables enabled retaining eight predictors (gender, sleep problems, Data analysis engagement in vigorous exercise, bereavement, obesity, Initially, as a screening criterion, we excluded variables self-rated health, multimorbidity and smoking), and with a relevant amount of missing that would have follow-up variables enabled retaining seven predictors dramatically reduced the sample size exploitable for the (energy level, difficulties with walking, respiratory present analysis. Second, predictors of pain were diseases, joint disorders, stroke and again self-rated assessed using univariable logistic regression models and health and sleep problems). The full results of multivari- those with a p-value< 0.10 were retained in the subse- able regression models are in supplementary materials quent analyses (see Table S2 in supplementary (see Table S3). The final sample size was composed of materials). 13,545 subjects, 8197 without pain and 5348 that Raggi et al. The Journal of Headache and Pain (2020) 21:45 Page 4 of 12 developed pain between baseline and follow-up, corre- factors, namely sleep problems and poorer self-rated sponding to 39.5% of the sample. health were retained among distal ones, but they were Table 1 provides full features of the final sample. Fe- discarded from the final model once proximal risk fac- males represented 53.3% of the entire sample, and aver- tors were included. age age moved from a median of 63 to a median of 68 What makes our results of such an importance is that years, and more than one-third of the sample was re- most of the risk factors herein included are modifiable, tired. Most descriptive variables were almost similar at either directly or indirectly. Factors that may be directly the two time points, with some exceptions: for example, addressed include sleep problems, obesity, engagement difficulty walking increased from 9.7% to 18.7%, and in vigorous activities, low energy/fatigue and walking dif- poor self-reported health from 5.1% to 8.9%. Among ficulties, whereas factors such as self-rated health can be health conditions, hypertension and joint disorders were targeted through general health improvement interven- the two most common at baseline (37.4% and 26.6%, re- tions, as well as through interventions aimed to address spectively) and underwent a significant increase (51.6% some of the aforementioned targets, such as sleep im- and 38.6% at follow-up, respectively), and multimorbid- provement and weight loss. In addition to this, we did ity was experienced by 27.3% of participants at baseline not include age as a variable in our analysis, but decided and 40.1% at follow-up. The same data are reported to include the ageing process itself, by weighting and study by study in supplementary materials (see Tables adjusting all our models for a time-lag variable, i.e. the S4, S5, S6, S7, S8, S9). years passing between the two time points in each study. Table 2 reports the results of the following models: Sleep problems may impact on pain in different ways, Model 1, stepwise-selected baseline predictors; Model 2, and are part of common associated symptomatology in significant baseline predictors only (p < 0.05); Model 3, different pain-related conditions affecting adults and significant baseline predictors and stepwise-selected older adults, such as low back pain, headache disorders follow-up predictors. All baseline variables, with the ex- or osteoarthritis [47–49]. As reported in a recent review clusion of smoking status and multimorbidity, were sig- [50] several hypotheses have been made. Disrupted sleep nificant risk factors for pain at 5.2 years’ follow-up. Once continuity (i.e. wakefulness during the night) may con- variables selected at follow-up were included, baseline tribute to increased pain perception (and higher opioids self-rated health was no more significant, whereas the consumption) through disruption of opioid circuits in- corresponding follow-up variable was. Among newly in- volved in the descending pain modulatory systems, and cluded variables, low-level energy/fatigue, difficulties involvement of inflammatory mechanisms typical of with walking, sleep problems and respiratory disease ageing-related conditions such as cardiovascular disor- were significant risk factors for pain occurrence, whereas ders, hypertension and diabetes, has also been addressed. neither joint disorders nor stroke were significant In fact, sleep disturbance were associated with increases predictors. in markers of systemic inflammation, such as Table 3 shows the results of the final model, where interleukin-6, which is known to sensitize nociceptors significant predictors only were retained. Being female, and increase pain sensitivity [51]. It has to be acknowl- engaging in vigorous exercise, being obese and bereave- edged that insomnia and reduced sleep hours are part of ment were all risk factors for the onset of pain over an the ageing process itself, with older people having diffi- average period of 5.2 years. In addition to this, low en- culty falling and staying asleep due to frequent arousals ergy levels, difficulties with walking, non-good self-rated [52]. Treatments are both pharmacological and non- health and presence of sleep problems were significant pharmacological [53–55]: among the first, benzodiaze- risk factors for the presence of pain at the follow up. pines, sedating low-dose antidepressants, antipsychotics and anticonvulsants are included, whereas non- Discussion pharmacological treatments mostly include cognitive be- The results of this study, based on a large sample of gen- havioural therapy. There is a growing interest on behav- eral ageing population, showed that 39.5% of respon- ioural treatment for sleep disturbances in older adults dents developed pain over a five-year follow-up period and elderlies, mostly in reason of their reduced risks and and that there are proximal and distal risk factors for hazards of sedative-hypnotics and opioids for middle- pain. Proximal risk factors include low energy level (or aged and older adults, as well as in consideration of the fatigue), walking difficulties, self-rated health perceived interaction between drugs prescribed for sleep problems as not good and sleep problems, whereas distal risk fac- and those for other medical conditions [51, 53]. tors – which predicted pain onset over a five-year time- Obesity is another known factor contributing to pain, frame – included female gender, engaging in vigorous through both inflammatory and mechanical processes exercise, being obese and suffering from the loss of a [56, 57]. Obesity is in fact a pro-inflammatory condition close person. It is interesting to notice that two risk and literature exists on the role of proinflammatory Raggi et al. The Journal of Headache and Pain (2020) 21:45 Page 5 of 12 Table 1 Final regression model sample description Baseline Follow-up No pain at Pain at Total No pain at Pain at follow-up Total follow-up follow-up (N = 13,545) follow-up (N = 5348) (N = 13,545) (N = 8197) (N = 5348) (N = 8197) Age, mean ± sd 59.8 ± 14.4 60.62 ± 13.0 60.1 ± 13.9 65.5 ± 13.9 64.9 ± 12.6 65.3 ± 13.5 [median: interquartile range] [64: 52–71] [62: 53–70] [63: 53–70] [70: 59–75] [66: 57–74] [68: 57–75] Sex (N = 13,545) (N = 13,545) Male 49.8% 41.9% 46.7% 49.8% 41.9% 46.7% Female 50.2% 58.1% 53.3% 50.2% 58.1% 53.3% Marital status (N = 13,529) (N = 13,526) Married/cohabiting 71.1% 71.4% 71.2% 67.2% 67.2% 67.2% Single 8.3% 8.0% 8.2% 7.5% 7.8% 7.6% Divorced/Separated 7.1% 7.1% 7.1% 7.7% 7.3% 7.6% Widow 13.5% 13.5% 13.5% 17.6% 17.7% 17.6% Education (N = 11,384) (N = 9931) Primary or less 28.6% 38.3% 32.5% 20.3% 31.6% 24.9% Secondary or above 71.4% 61.7% 67.5% 79.7% 68.4% 75.1% Retired (N = 12,984) (N = 12,974) 31.2% 34.3% 32.5% 41.9% 40.5% 41.3% Household wealth quintile (N = 13,140) (N = 10,175) 1 - Lower 15.7% 20.0% 17.4 19.5% 23.3% 21.0% 2 17.6% 19.0% 18.2 18.4% 21.7% 19.7% 3 19.7% 17.9% 19.0 20.1% 19.7% 19.9% 4 23.5% 22.3% 23.0 21.1% 17.5% 19.7% 5 - Higher 23.5% 20.8% 22.4 20.9% 17.8% 19.7% Smoking status, (N = 12,674) (N = 10,629) Never 47.4% 51.4% 48.9% 44.0% 44.2% 44.0% Former smoker 31.7% 25.1% 29.3% 39.8% 30.1% 36.9% Current 20.9% 23.5% 21.8% 16.2% 25.7% 19.1% Current alcohol consumption (N = 13,541) (N = 13,008) 57.2% 56.8% 57.1% 55.5% 56.0% 55.7% Engaged in vigorous exercise (N = 13,545) (N = 11,901) 25.6% 38.7% 30.7% 21.4% 33.7% 26.3% Low level of energy/Fatigue (N = 13,413) (N = 13,545) 25.7% 34.0% 28.9% 21.6% 43.8% 30.3% Sleep problems (N = 13,430) (N = 13,545) 24.8% 35.9% 29.1% 25.8% 45.1% 33.3% Difficulty walking by yourself (N = 12,972) (N = 13,545) 8.4% 11.6% 9.7% 14.1% 25.9% 18.7% Self-reported health (N = 12,818) (N = 13,545) Poor 3.5% 7.5% 5.1% 4.8% 15.1% 8.9% Average/Fair/Moderate 19.6% 27.2% 22.6% 21.7% 34.6% 26.7% Good 76.9% 65.3% 72.3% 73.5% 50.3% 64.4% Recent falls (N = 9656) (N = 10,092) 26.2% 64.6% 42.4% 30.7% 68.2% 46.3% Evaluative wellbeing, (N = 7178) (N = 9467) Raggi et al. The Journal of Headache and Pain (2020) 21:45 Page 6 of 12 Table 1 Final regression model sample description (Continued) Baseline Follow-up No pain at Pain at Total No pain at Pain at follow-up Total follow-up follow-up (N = 13,545) follow-up (N = 5348) (N = 13,545) (N = 8197) (N = 5348) (N = 8197) Low 5.7% 22.2% 14.4% 6.3% 25.0% 15.2% Middle 20.8% 27.9% 24.5% 18.6% 28.2% 23.2% High 73.5% 49.9% 61.1% 75.1% 46.8% 61.6% Diabetes (N = 11,928) (N = 12,214) 11.8% 15.7% 13.1% 19.0% 19.6% 19.2% Respiratory diseases (N = 13,531) (N = 12,084) 8.0% 9.2% 8.5% 10.7% 14.0% 12.0% Hypertension (N = 13,527) (N = 12,609) 37.5% 37.4% 37.4% 50.8% 52.8% 51.6% Joint disorders (N = 13,537) (N = 12,688) 26.6% 26.7% 26.6% 347% 44.4% 38.6% Angina (N = 3453) (N = 3453) 1.5% 2.2% 1.7% 2.8% 4.5% 3.4% Stroke (N = 13,535) (N = 12,084) 1.1% 1.9% 1.4% 1.7% 2.7% 2.1% Cancer (N = 11,922) (N = 11,503) 1.7% 3.2% 2.3% 2.3% 4.6% 3.2% Multimorbidity (N = 13,544) (N = 13,096) 27.6% 27.0% 27.3% 38.4% 42.6% 40.1% Obesity (N = 13,545) (N = 11,245) 19.0% 23.1% 20.6% 21.2% 24.5% 22.5% Depression (N = 13,302) (N = 13,451) 10.6% 22.3% 15.2% 10.6% 29.8% 18.1% Experience of a loss of any close person (bereavement) (N = 13,545) (N = 13,536) 28.6% 44.5% 34.8% 33.4% 58.3% 43.1% cytokines in producing a hyperalgesic state. Excess of reduce up to 50% the risk of developing osteoarthritis on adipose tissue may in fact lead to an increased inflam- a 10-year period [66]. matory response with through different chemical media- Exercising is protective for health in general, as also tors involved in inflammation. Parallel to this, obesity stated in the recent consensus statement on physical ac- determines increased risk and severity of musculoskel- tivity and ageing [67] where, however, an explicit men- etal conditions, such as back pain [58]. In fact, the over- tion to the fact that benefits for health among older load over low back, hip and knee joints cause injury and adults can be realised at lower volumes and intensity degradation to the cartilage and bone matrix in these than with the usually recommended “150min of moder- structures, leading to osteoarthritis. In addition to this, ate to vigorous intensity physical activity per week” is known comorbidities of obesity that might exacerbate made. Besides exposing people to a higher risk of sport- pain have to be taken into account, in particular sleep related injuries, moderate to vigorous exercise is predict- disorders (e.g. obstructive sleep apnoea) and depression ive of the onset of back pain among ageing populations [57, 59–61]. Evidence exist that weight loss programs, [58, 68], worsening of osteoarthritis-related pain severity which include diet therapy and physical activity or, in [69]. Therefore, older adults and aging population specific cases, bariatric surgery, have positive effect on should engage in mild to moderate, rather than vigorous, pain reduction in patients with different types of chronic exercise. musculoskeletal pain [62], osteoarthritis [63], back and Pain and fatigue have been found to be connected in knee pain [64], and migraine [65], and it showed to several conditions, in particular musculoskeletal Raggi et al. The Journal of Headache and Pain (2020) 21:45 Page 7 of 12 Table 2 Hierarchical pain predictive models: stepwise-selected baseline predictors, significant baseline predictors only (p < 0.05), significant baseline predictors and stepwise-selected follow-up predictors Model 1: stepwise-selected Model 2: baseline with Model 3: significant baseline baseline predictors significant predictors only predictors and stepwise- (N = 16,097) (N = 16,979) selected follow-up predictors (N = 11,852) OR (95% CI) Z (p-value) OR (95% CI) Z (p-value) OR (95% CI) Z (p-value) Baseline Variables Female gender 1.39 (1.24–1.56) 5.51 (<.001) 1.42 (1.28–1.58) 6.49 (<.001) 1.36 (1.18–1.56) 4.34 (<.001) Engage in vigorous exercise 1.50 (1.32–1.71) 6.10 (<.001) 1.73 (1.53–1.95) 8.93 (<.001) 3.10 (2.64–3.64) 13.69 (<.001) Bereavement 1.31 (1.17–1.46) 4.59 (<.001) 1.50 (1.35–1.67) 7.44 (<.001) 1.90 (1.64–2.20) 8.52 (<.001) Obesity 1.23 (1.09–1.38) 3.45 (.001) 1.22 (1.09–1.36) 3.37 (<.001) 1.17 (1.00–1.36) 1.97 (.049) Self-rated health – average to moderate (vs. good) 1.65 (1.44–1.89) 7.23 (<.001) 1.75 (1.54–1.99) 8.64 (<.001) 1.07 (0.89–1.28) 0.68 (.499) Self-rated health – poor (vs. good) 2.24 (1.78–2.81) 6.97 (<.001) 2.26 (1.82–2.81) 7.35 (<.001) 0.95 (0.61–1.48) −0.23 (.819) Sleep problems 1.35 (1.21–1.52) 5.13 (<.001) 1.41 (1.26–1.57) 5.98 (<.001) 1.25 (1.07–1.46) 2.78 (.005) Multimorbidity 1.10 (0.98–1.24) 1.60 (.109) – – – – Smoking – Former smoker (vs. never smoker) 0.87 (0.77–1.00) −2.02 (.043) – – – – Smoking – Current smoker (vs. never smoker) 1.15 (0.98–1.24) 1.91 (.056) – – – – Follow-up Variables Low energy level/Fatigue 1.97 (1.67–2.32) 8.15 (<.001) Self-rated health – average to moderate (vs. good) 2.32 (1.94–2.78 9.19 (<.001) Self-rated health – poor (vs. good) 3.80 (2.57–5.60) 6.71 (<.001) Difficulties with walking 1.52 (1.26–1.83) 4.35 (<.001) Sleep problems 1.88 (1.61–2.18) 8.13 (<.001) Respiratory disease 1.26 (1.03–1.56) 2.19 (.028) Joint disorders 0.96 (0.83–1.11) −0.57 (.571) Stroke 0.90 (0.54–1.52) −0.38 (.702) Notes. Model 1: Hosmer-Lemeshow’s p-value = .085; AUC 0.64 (95% CI: 0.63–0.65). Model 2: Hosmer-Lemeshow’s p-value = .038; AUC 0.65 (95% CI: 0.64–0.66). Model 3: Hosmer-Lemeshow’s p-value = .010; AUC 0.79 (95% CI: 0.78–0.80) Table 3 Final hierarchical pain predictive model Model 4: significant baseline and follow-up predictors (N = 13,545) OR (95%CI) Z (p-value) Baseline Variables Female gender 1.34 (1.18–1.51) 4.61 (<.001) Engage in vigorous exercise 2.51 (2.18–2.89) 12.71 (<.001) Bereavement 1.88 (1.65–2.15) 9.39 (<.001) Obesity 1.36 (1.18–1.57) 4.20 (<.001) Follow-up Variables Low energy level/Fatigue 1.93 (1.68–2.20) 9.27 (<.001) Self-rated health – average to moderate (vs. good) 1.57 (1.36–1.82) 6.04 (<.001) Self-rated health – poor (vs. good) 2.20 (1.71–2.83) 6.13 (<.001) Difficulties with walking 1.69 (1.43–1.99) 6.12 (<.001) Sleep problems 1.80 (1.58–2.05) 8.97 (<.001) Note: Hosmer-Lemeshow’s p-value = .001; AUC 0.77 (95% CI 0.76–0.78) Raggi et al. The Journal of Headache and Pain (2020) 21:45 Page 8 of 12 disorders such as rheumatoid arthritis and neuromuscu- overlapping [83–85], and depression was found in lar disorders such as fibromyalgia and chronic fatigue 18% to 55% of bereaved people [86–88]. Our hypoth- syndrome, and it is besides associated to poor sleep and esis is that in a sample of ageing population like the symptoms of anxiety and depression [70–73]. However, one we analysed – where one-third of participants at the way in which fatigue exerts its effect on pain is un- baseline reported the experience of the loss of a close clear, but some hypotheses have been postulated, which person, and 15% reported depression – the experience include a common pathway mediated by inflammatory of bereavement confounded and overcame the pre- processes, such as increased levels of pro-inflammatory dictive power of depression towards pain prediction. cytokines, and sensitisation of muscle nociceptors [72, In addition to this, have no indication on the amount 74]. Treatment strategy for fatigue include nutraceuti- of time that passed between the loss experience and cals, although with conflicting results [75–77], and exer- the interview, and on the presence of a problematic cise therapy [78]. grief among bereaved people in our sample. Walking difficulties can be the outcome of several Some limitations have to be acknowledged. First, health problems – such as stroke, obesity or osteoarth- the variables used for our analysis are from a harmo- ritis – and thus require disease-specific treatments. For nized dataset, with a clear simplification from their example, in post-stroke patients gait difficulties are very original formulation. The main outcome definition is common and generally due to impairments in muscle “self-reported pain experienced at the time of the strength, muscle tone, control over voluntary movement interview”, with a dichotomous output, and, for most and balance, which result in reduced walking speed, of the studies herein used, the original items ad- temporal and spatial inter-limb asymmetries or impaired dressed pain in terms of severity and impact of pain, balance control [79]. Therefore, approaches to gait and which spans between mild and disabling. Another walking rehabilitation in stroke survivors will need to variable for which relevant differences exist is “energy address different impairments, making it really a patient- level” which in some studies (e.g. COURAGE in Eur- specific treatment. Among obese subjects, walking im- ope) was addressed in terms of the amount of energy pairments are often due to knee osteoarthritis [80] and the person had (with response options varying be- respiratory impairment or comorbidities [81, 82], and re- tween “not at all” and “completely”) [89], whereas habilitation programmes for obesity reduction include other studies (e.g. CHARLS) addressed it in terms of moderate exercise aimed to improve walking, with posi- fatigue or “feeling that everything is an effort” (with tive effect on pain outcomes [62–64]. response options based on frequency, from “rarely” to Conversely, our final model did not include some “most or all of the time”) [90]. Second, there is a risk factors that have been found in previous litera- considerable variation in the lag between waves across ture, including sociodemographic data such as educa- studies, which varied between 2 and 11 years: we in- tion and unemployment [35, 36], risk factors such as cluded the lag variable so to interpret predictors con- smoking [44], and health condition such as depres- trolling for the difference between respondents’ ageing sion, diabetes, stroke, musculoskeletal diseases and across studies. Third, stepwise regression, which had multimorbidity status [6, 26, 28–30, 32, 34, 35, 39– the merit to enable reducing the amount of predictors 41]. Some of them, namely smoking status vs. never to significant ones, present intrinsic problems, in par- smoking, multimorbidity, joint disorders or stroke, ticular biased high R2 values, small standard error were included in the model, but not retained when and narrow 95% CI for odd ratios, small and difficult other variables were included or when the final model to correct p-values due to multiple comparisons and with significant predictors only was run. The reason exacerbated collinearity problems [91]. Fourth, when for this lack of predictive power is likely due to the we selected the variables to be included in the large amount of variable we used, most of which were models, we had to face the problems of variable miss- significantly associated in univariable analyses (see ing values, which prevented some of them from being Table S2 in supplementary materials). A separate note included (e.g. the variables “History of angina” and has to be made for depression which was not selected “Myocardial infarction or heart attack” had 51% and from the stepwise logistic regression. Our data 55% of missing, respectively, whereas “recent falls” showed in fact that bereavement, i.e. the loss of a which his intuitively of importance had 47% of miss- close person, was predictive of pain over a five-year ing). Possible solutions preserving to loose sample period, whereas depression was not. Some studies would have included treating them as a separate cat- showed that in some bereaved individuals the death egory by itself and data imputation. The problem with of a loved one precipitates a combination of symp- the first is that it would have led to biased estimation toms of both grief and depression, as well as of trau- in regression coefficients. Imputation of missing matic stress, although the categories are not values, e.g. through mean imputation or regression- Raggi et al. The Journal of Headache and Pain (2020) 21:45 Page 9 of 12 based imputation, was not considered in reason of - Collaborative Research on Ageing (COURAGE) in Europe: The COURAGE the very limited set of records, namely around 2000 study was supported by the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement number 223071 out of the 50,849 not referring pain at baseline and (COURAGE in Europe). Data from Spain were also collected with support with complete pain information at follow-up, with from the Instituto de Salud Carlos III-FIS research grants number PS09/00295, complete information for the remaining variables of PS09/01845, PI12/01490, PI13/00059, PI16/00218 and PI16/01073; the Spanish Ministry of Science and Innovation ACI-Promociona (ACI2009-1010); the Euro- interest: the reliability of a model with such a large pean Regional Development Fund (ERDF) ‘A Way to Build Europe’ grant amount of imputed values would have been at least numbers PI12/01490 and PI13/00059; and by the Centro de Investigación debatable. 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 Conclusions (number 1277/7PR/UE/2009/7, 2009–2012) and Jagiellonian University Med- ical College grant for project COURAGE-POLFUS (K/ZDS/005241). The authors In conclusion, we showed that over a five-year period gratefully acknowledge the work of COURAGE researchers who provided 39.5% of respondents from this ageing population that data for this paper. did not experience pain at baseline, developed pain at - The Health 2000/2011 study: The authors gratefully acknowledge the Finnish Institute for Health and Welfare who provided data for this paper. follow-up, and that there are distal and proximal risk - Health and Retirement Study (HRS): The HRS study is supported by the factors for new pain onset. The first group included fe- National Institute on Aging (grant number NIA U01AG009740) and the Social male gender, engaging in vigorous activities, being obese Security Administration, and is conducted by the University of Michigan. The authors gratefully acknowledge the University of Michigan who provided and suffering from the loss of a close person, whereas data for this paper. the second included low energy level (or fatigue), walk- - The Mexican Health and Aging Study (MHAS): The MHAS study is partly ing difficulties, self-rated health perceived as not good sponsored by the National Institutes of Health/National Institute on Aging (grant number NIH R01AG018016) and the INEGI in Mexico. The authors and sleep problems. Part of these predictors are directly gratefully acknowledge the MHAS team who provided data for this paper modifiable: therefore, it is expectable that actions aimed retrieved from www.MHASweb.org at improving sleep, reducing weight among obese people - The Survey of Health, Ageing and Retirement in Europe (SHARE): The SHARE study is funded by the European Commission through FP5 (QLK6-CT- and treating fatigue would positively impact on pain on- 2001–00360), FP6 (SHARE-I3: RII-CT-2006–062193, COMPARE: CIT5-CT-2005– set. In addition, indication to avoid vigorous exercise 028857, SHARELIFE: CIT4-CT-2006–028812) and FP7 (SHARE-PREP: N°211909, should also be provided to people aged around 60 years SHARE-LEAP: N°227822, SHARE M4: N°261982). Additional funding from the German Ministry of Education and Research, the Max Planck Society for the or older, in particular if female or obese. Advancement of Science, the U.S. National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, Supplementary information R21_AG025169, Y1-AG-4553–01, IAG_BSR06-11, OGHA_04-064, Supplementary information accompanies this paper at https://doi.org/10. HHSN271201300071C) and from various national funding sources is gratefully 1186/s10194-020-01116-3. acknowledged (see www.share-project.org). Alberto Raggi is supported by a grant from the Italian Ministry of Health (Ricerca Corrente, Fondazione Istituto Neurologico C. Besta, Linea Additional file 1: Table S1. Description of exploited studies and waves. 4—Outcome Research: dagli Indicatori alle Raccomandazioni Cliniche). Table S2. Univariable logistic regression results. Table S3. Multivariable Stefanos Tyrovolas was supported by the Foundation for Education and logistic regression results. Table S4. Descriptive statistics, China Health European Culture, the Miguel Servet programme (reference CP18/00006), and Retirement Longitudinal Study (CHARLS). Table S5. Descriptive and the Fondos Europeos de Desarrollo Regional. statistics Collaborative Research on Ageing in Europe Study (COURAGE in Europe). Table S6. Descriptive statistics Health and Retirement Study Authors’ contributions (HRS). Table S7. Descriptive statistics Health 2000/2011 study (Health AR, planned the analyses, drafted the manuscript and revised it for 2000/2011). Table S8. Descriptive statistics Mexican Health and Aging intellectual content; ML, planned the analyses and revised the manuscript for Study (MHAS). Table S9. Descriptive statistics Survey of Health, Ageing intellectual content; BMM, MVM, ASN, ST, IGV, JMH, SC, MB, JLAM, HA, SK, JB, and Retirement in Europe (SHARE). ZH, BTA and DP revised the manuscript for intellectual content; BC, planned and ran the analyses. The authors read and approved the final manuscript. Abbreviations ATHLOS: Ageing Trajectories of Health – Longitudinal Opportunities and Funding Synergies; AUC: Area Under the Receiver Operating Curve; BMI: Body Mass The ATHLOS project (Ageing Trajectories of Health: Longitudinal Index; CHARLS: China Health and Retirement Longitudinal Study; COURAGE Opportunities and Synergies) has received funding from the European in Europe: Collaborative Research on Ageing in Europe; HRS: Health and Union’s Horizon 2020 research and innovation programme under grant Retirement Study; IQR: Interquartile ranges; MHAS: Mexican Health and Aging agreement No 635316. The funding body had no role in the design of the Study; OR: Odds ratio; SHARE: Survey of Health, Ageing and Retirement in study, analysis and interpretation of data, and in writing the manuscript. Europe; SD: Standard deviation; VIF: Variance Inflation Factor; 95% CI: 95% Confidence Intervals Availability of data and materials The datasets generated and/or analysed during the current study are not Acknowledgements publicly available due restrictions imposed by part of the owners (COURAGE in The ATHLOS project researchers are grateful for data contribution and Europe, the Health 2000 and 2011 Surveys- Finland), but may be available from funding in the following studies: the corresponding author upon reasonable request and once consent form - The China Health and Retirement Longitudinal Study (CHARLS): The ATHLOS project intellectual property and dissemination board is obtained. CHARLS study has received critical support from Peking University, the National Natural Science Foundation of China, the Behavioral and Social Ethics approval and consent to participate Research Division of the National Institute on Aging and the World Bank. The Not applicable for ATHLOS study: ethics approval and participants’ consent authors gratefully acknowledge the work of the project team at the Peking to participate were obtained by each study at the time point of surveys’ field University who provided data for this paper. trials. Raggi et al. The Journal of Headache and Pain (2020) 21:45 Page 10 of 12 Consent for publication 13. Sanchez-Niubo A, Egea-Cortés L, Olaya B, Caballero FF, Ayuso-Mateos JL, Not applicable. Prina M, Bobak M, Arndt H, Tobiasz-Adamczyk B, Pająk A, Leonardi M, Koupil I, Panagiotakos D, Tamosiunas A, Scherbov S, Sanderson W, Koskinen S, Competing interests Chatterji S, Haro JM (2019) ATHLOS Consortium. Cohort profile: the ageing The authors declare that they have no competing interests. trajectories of health – longitudinal opportunities and synergies (ATHLOS) project. Int J Epidemiol 48:1052–1053i Author details 14. Guido D, Leonardi M, Mellor B, Moneta MV, Sanchez-Niubo A, Tyrovolas S, 1 Neurology, Public Health and Disability Unit, Fondazione IRCCS Istituto Giné-Vázquez I, Haro JM, Chatterji S, Bobak M, Ayuso-Mateos JL, Arndt H, Neurologico Carlo Besta, Via Celoria 11, 20133 Milan, Italy. 2Parc Sanitari Sant Koupil I, Bickenbach J, Koskinen S, Tobiasz-Adamczyk B, Panagiotakos D, Joan de Déu, Fundacion Sant Joan de Deu, Barcelona, Spain. 3Instituto de Raggi A (2020) Pain rates in general population for the period 1991–2015 Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, and 10-years prediction: results from a multi-continent age-period-cohort CIBERSAM, Madrid, Spain. 4Department of Nutrition and Dietetics, School of analysis. J Headache Pain. https://doi.org/10.1186/s10194-020-01108-3. Health Science and Education, Harokopio University, Athens, Greece. 15. Harrison JM, Lagisetty P, Sites BD, Guo C, Davis MA (2018) Trends in 5 Information, Evidence and Research, World Health Organization, Geneva, prescription pain medication use by race/ethnicity among us adults with Switzerland. 6Research Department of Epidemiology and Public Health, noncancer pain, 2000-2015. Am J Public Health 108:788–790 University College London, London, UK. 7Department of Psychiatry, 16. Negro A, Sciattella P, Rossi D, Guglielmetti M, Martelletti P, Mennini FS Universidad Autónoma de Madrid, Madrid, Spain. 8Hospital Universitario de (2019) Cost of chronic and episodic migraine patients in continuous La Princesa, Instituto de Investigación Sanitaria Princesa, Madrid, Spain. treatment for two years in a tertiary level headache Centre. J Headache Pain 9 SPRING TECHNO GMBH & Co. KG, Bremen, Germany. 10Department of 20(1):120 Global Public Health, Karolinska Institute, Stockholm, Sweden. 11Department 17. Ropponen A, Narusyte J, Mittendorfer-Rutz E, Svedberg P (2019) Number of of Health Sciences and Health Policy, University of Lucerne, Lucerne, pain locations as predictor of cause-specific disability pension in Sweden- Switzerland. 12Swiss Paraplegic Research, Nottwil, Switzerland. 13Finnish do common mental disorders play a role? J Occup Environ Med 61:646–652 Institute for Health and Welfare (THL), Helsinki, Finland. 14Department of 18. Raggi A, Corso B, De Torres L, Quintas R, Chatterji S, Sainio P, Martinuzzi A, Epidemiology and Population Studies, Faculty of Health Sciences, Zawisza K, Haro JM, Minicuci N, Leonardi M (2018) Determinants of mobility Jagiellonian University Medical College, Krakow, Poland. 15National Research in populations of older adults: results from a cross-sectional study in Council, Neuroscience Institute, Padova, Italy. Finland, Poland and Spain. Maturitas 115:84–91 19. 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