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Chronic pain and its association with obesity among older adults in China
Archives of Gerontology and Geriatrics 76 (2018) 12–18
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Chronic pain and its association with obesity among older adults in China a,b
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Jie Li , Jian Chen , Qirong Qin , Dongdong Zhao , Bao Dong , Qiongqiong Ren , Dandan Yu , ⁎ Peng Bia,d, Yehuan Suna,e, a
Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China School of Public Health, Wannan Medical College, Wuhu, Anhui, China Ma’anshan Center for Disease Control and Prevention, Ma’anshan, Anhui, China d School of Public Health, The University of Adelaide, Adelaide, South Australia, Australia e Center for Evidence-Based Practice, Anhui Medical University, Hefei, Anhui, China b c
A R T I C L E I N F O
A B S T R A C T
Keywords: China Chronic pain Cross-sectional survey Elderly Obesity
Objectives: There is a paucity of epidemiological data on chronic pain and obesity among older adults. This study attempted to present the characterization of chronic pain and its association with obesity among the Chinese elderly. Methods: A cross-sectional survey was undertaken among 6524 elderly individuals aged ≥60 years in China. Chronic pain was identified by self-reports based on the definition from the International Association for the Study of Pain (IASP). Body Mass Index (BMI) was measured to assess obesity. Binary logistic regression analysis was performed to explore the association between obesity and chronic pain. Results: The prevalence of chronic pain was 49.8%. The legs/feet (25.5%), back (23.2%), and neck/shoulder (14.6%) were the most salient locations for chronic pain. Compared with normal weight, subjects with overweight (OR = 1.234, 95%CI = 1.100–1.384) and obesity (OR = 1.715, 95%CI = 1.418–2.073) were considerably more likely to have chronic pain after adjusting for covariates (p < .05). Age was not significantly associated with chronic pain (p > .05). Further analyses revealed that the associations between chronic pain and obesity were restricted to the legs/feet and back. Conclusion: Chronic pain is common among older adults in China. Understanding the role of obesity in chronic pain is important for preventing and treating chronic pain.
1. Introduction According to the International Association for the Study of Pain (IASP), pain is defined as an unpleasant sensory and emotional experience associated with actual or potential tissue damage or described in terms of such damage, and it is classified as chronic pain if it persists for longer than 3 months (IASP, 1986). Surveys showed that the prevalence of chronic pain was 37.3% in developed countries and 41.1% in developing countries among the general adult population (Tsang et al., 2008). Age is often considered to be associated with chronic pain (Chen et al., 2016; Lamerato et al., 2016). A systematic review in Asian adults showed that the prevalence of chronic pain among adults aged 18 years and above varied from 7.1% to 61%, while the prevalence varied from 42% to 90.8% among the Asian geriatric population (3 studies) (Mohamed Zaki & Hairi, 2015). However, some studies showed that the prevalence of chronic pain was not associated with age in elderly people (Satghare et al., 2016; Patel, Guralnik, Dansie, & Turk, 2013).
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Researchers have not reached a consistent conclusion that there is a flat or increasing prevalence of chronic pain among older adults. The age pattern of chronic pain in the elderly is not well established due to very few studies in older adults. Since 1980, the prevalence of obesity has doubled in more than 70 countries and has continuously increased in most countries (GBD Obesity Collaborators, 2017). The disease burden related to high BMI has increased accordingly. A number of studies over the last few decades have examined the association between obesity and pain (Malta et al., 2017; Tanamas et al., 2012). A survey of 1062271 individuals in the United States showed that BMI and pain are positively correlated: the overweight group reported 20% higher rates of pain than the lownormal group, 68% higher for the obese Ⅰ group, 136% higher for the obese Ⅱ group, and 254% higher for the obese Ⅲ group (Stone & Broderick, 2012). The association between chronic pain and obesity is important, since obesity is also associated with increased pain-related disability and reduced physical functioning (Marcus, 2004).
Corresponding author at: Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan road, Hefei, Anhui 230032, China. E-mail address: [email protected] (Y. Sun).
https://doi.org/10.1016/j.archger.2018.01.009 Received 7 November 2017; Received in revised form 24 January 2018; Accepted 25 January 2018 0167-4943/ © 2018 Elsevier B.V. All rights reserved.
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four groups: underweight (< 18.5 kg/m2), normal weight (18.5–24.0 kg/m2), overweight (24.0–28.0 kg/m2) and obese (≥28.0 kg/m2), based on Chinese standards of BMI (Zhou, 2002).
Nevertheless, the association between chronic pain and obesity has not been fully studied. Data on the association between chronic pain and obesity in the elderly population is limited. Given that older adults have a different state of health, such as decreased metabolic capacity and decreased sensitivity to pain, it is necessary to clarify the association between chronic pain and obesity in the elderly population. The increasing ratio trend of the aging population is well underway, while the overall global population is declining (Fuster, 2017). Within the context of aging, China entered the stage of population aging in 2000, and the proportion of aging increased year by year. The proportion of people aged 60 and above was 16.14% in 2015 (National Bureau of Statistics of China, 2016). Studies showed that chronic pain may have significant impacts on health and well-being, such as daily activity impairments, and on lower quality of life (Inoue et al., 2015; Takura et al., 2015). It is of great significance for healthy aging to prevent chronic pain. Therefore, the aims of this study were to investigate the following: (1) the prevalence and age pattern of chronic pain among the Chinese elderly; and (2) the associations between chronic pain and obesity.
2.2.3. Socio-demographic (or other) variables The following socio-demographic variables were included in this survey: sex (men, women), age (60–69, 70–79, ≥80 years), education (illiterate, primary, secondary or above), marital status (single, including never married, divorced, widowed; married), living alone (no, yes), monthly income (< 200 RMB, 200–1000 RMB, > 1000 RMB), area (rural, urban), current smoking (no, yes), current alcohol consumption (no, yes), chronic diseases (no; yes, including hypertension, diabetes mellitus, coronary heart disease, arrhythmia, stoke, chronic lung diseases, chronic stomach diseases, cancer, cataract, glaucoma, cholecystitis, and nephritis based on final diagnosis from medical institutions). Activity of Daily Living (ADL) was measured using a 14-item scale (Physical Self-Maintenance Scale and Instrumental Activity of Daily Living). The sum scores ranged from 4 to 64. It was classified into three categories: normal function (sum score < 16), decline in function (sum score: 16–21), and significant dysfunction (sum score ≥22 or two or more items scores greater than three) (Su et al., 2011). Depression was measured by the GDS-15 (15-item Geriatric Depression Scale). This scale contains 15 items (score: 0–15), with higher scores indicating poorer depressive symptoms. A cutoff score of ≥8 is defined as depression (Woo et al., 1994). Cognitive assessment was performed by Mini-Mental State Examination (MMSE), which is widely used to assess cognitive function (Folstein, Folstein, & McHugh, 1975). It contains 30 items, with scores ranging from 0 to 30. A higher score corresponds to a better cognitive state.
2. Methods 2.1. Participants This study was conducted in Ma’anshan, a municipality located in eastern Anhui Province, China. The study participants were derived from two stages. First, we investigated the rural elderly population using a cluster random sample approach. We contacted 27 rural villages in Dangtu County of Ma’anshan City in September to October 2016. Second, we investigated the urban elderly in December 2016 to March 2017. Three community health centers were randomly selected. To improve the response rate, we presented a small incentive gift to the participants. Furthermore, we provided free physical examination services for them, such as measuring blood pressure to promote participation. In addition, the Ma’anshan Centers for Disease Control also provided many forms of support for this investigation. Nearly half of the older adults were illiterate in this investigation. Thus, we explained the contents of the questionnaire to the participants with the help of graduate students and other local older adults. A total of 6881 elderly aged 60 years or above were included. Face-to-face interviews and physical examinations were conducted. After the completion of the questionnaire, the investigators needed to check the integrity and logic of the questionnaire. Eventually, 6524 older adults were successfully interviewed. The overall response rate was 94.8%. Three weeks later, 200 elderly people were selected to retest the content of the questionnaire. The Ethical approval was granted by the ethics committee of Anhui Medical University.
2.3. Statistical analysis All statistical analyses for this study were conducted using SPSS 16.0 (SPSS for Windows, Version 16.0, Chicago, SPSS Inc.). A pvalue < .05 was designated as showing a significant difference. The differences between chronic pain and characteristics of the elderly were examined using χ2 tests and t-tests. The differences between chronic pain locations and BMI and age were compared using χ2 tests. Binary logistic regression analyses were applied to assess the associations between chronic pain and obesity. The dependent variable was chronic pain (no = 0, yes = 1). 3. Results 3.1. General characteristics of subjects The study population consisted of 3685 women (56.5%) and 2839 men (43.5%). Participants were on average 71.09 ± 6.73 years of age, and 44.3% of them were 60–69 years. Nearly half of them (48.0%) were illiterate. Most participants were married (76.6%) and living with a spouse or other family members (84.1%). A total of 3048 (46.7%) older adults came from rural areas, and 3476 (53.3%) older adults came from urban areas. Table 1 shows the descriptive statistics of the older adults.
2.2. Measures 2.2.1. Chronic pain In the present study, chronic pain was defined using the IASP definition. First, subjects were asked “Did you have pain in the past 6 months lasting ≥3 months in duration?” If yes, 8 locations (head, face, neck and shoulder, back, arms and hands, legs and feet, chest, and abdomen and pelvis) were given (McCarthy, Bigal, Katz, Derby, & Lipton, 2009). One or more locations could be selected according to the actual condition. Subjects were then asked to rate their pain intensity. Pain intensity was assessed using a numerical rating scale (NRS) score (Gianni et al., 2010). It is scored between 0 (no pain) and 10 (worst possible pain). The total pain intensity was identified as the highest pain intensity of the locations.
3.2. Characteristics of chronic pain The retest reliability of chronic pain was 0.89. The prevalence of chronic pain was 49.8% (3250) among the older adults in this sample. Compared with the population with no pain, the population with chronic pain had a higher proportion of women, older individuals, those with lower levels of education, those who were single in terms of marital status, those who had lower incomes, and those who lived in rural areas (p < .05). Among all the chronic pain participants, a total of 1963 (60.4%) participants reported pain in one location, and 1287 (39.6%) participants reported pain in two or more locations. The
2.2.2. Obesity Weight and height were measured during the face-to-face interview. BMI is calculated as weight (kg)/[height (m)]2. It is categorized into 13
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3.3.2. Chronic pain and obesity in univariate analyses There was a significant association between chronic pain and BMI (p < .05). A total of 60.8% of the participants with obesity had chronic pain, while 46.9% of the participants with normal weight had chronic pain. Table 3 presents the associations between chronic pain locations and BMI category. The significant associations existed in the back and in the legs/feet (p < .05). No significant results were found between BMI category and chronic pain in other locations.
Table 1 Characteristics of the elderly according to chronic pain (n = 6524). Characteristics
Sex Men (%) Women (%) Age (years) 60–69 (%) 70–79 (%) ≥80 (%) Education Illiterate (%) Primary (%) Secondary or above (%) Marital status Single (%) Married (%) Living alone No (%) Yes (%) Monthly income (yuan) < 200 (%) 200–1000 (%) > 1000 (%) Area Rural (%) Urban (%) Current smoking No (%) Yes (%) Current alcohol consumption No (%) Yes (%) Chronic diseases No (%) Yes (%) Activity of Daily Living Normal function (%) Decline in function (%) Significant dysfunction (%) BMI (kg/m2) Depression (score) MMSE (score) *
N
p-value*
Chronic pain No
Yes
2839 3685
1714 (60.4) 1560 (42.3)
1125 (39.6) 2125 (57.7)
2887 2847 790
1508 (52.2) 1388 (48.8) 378 (47.8)
1379 (47.8) 1459 (51.2) 412 (52.2)
3122 1635 1767
1273 (40.8) 902 (55.2) 1099 (62.2)
1849 (59.2) 733 (44.8) 668 (37.8)
1524 5000
626 (41.1) 2648 (53.0)
898 (58.9) 2352 (47.0)
5489 1035
2862 (52.1) 412 (39.8)
2627 (47.9) 623 (60.2)
< 0.001
0.012
3.4. Binary logistic regression analyses of chronic pain The results of binary logistic regression analyses between chronic pain and obesity are listed in Table 4. Model 1 showed that BMI and age were associated with chronic pain. After including socio-demographic and other variables in model 3, the results showed that women, rural participants, and those with lower monthly incomes, chronic diseases, depression, and significant dysfunction of ADL were more likely to report chronic pain. However, no association was found between chronic pain and age. Overweight (OR = 1.224, 95%CI = 1.079–1.389) and obesity (OR = 1.697, 95%CI = 1.389–2.073) were significantly associated with chronic pain in model 3. The results of binary logistic regression analyses between chronic pain locations and obesity are listed in Table 5. Chronic pain in the back and legs/feet was associated with obesity, after adjusting for sex, age, education, marital status, living status, monthly income, area, current smoking, current alcohol consumption, chronic diseases, depression, ADL, and MMSE.
< 0.001
< 0.001
< 0.001
< 0.001 1398 1676 3450
474 (33.9) 713 (42.5) 2087 (60.5)
924 (66.1) 963 (57.5) 1363 (39.5)
3048 3476
1115 (36.6) 2159 (62.1)
1933 (63.4) 1317 (37.9)
5604 920
2737 (48.8) 537 (58.4)
2867 (51.2) 383 (41.6)
< 0.001
< 0.001
4. Discussion < 0.001
5676 848
2794 (49.2) 480 (56.6)
2882 (50.8) 368 (43.4)
1322 5202
904 (68.4) 2370 (45.6)
418 (31.6) 2832 (54.4)
This cross-sectional epidemiological study showed the characteristics of chronic pain and its associations with obesity among older adults in China. Nearly half of the participants had chronic pain. The results revealed a significant association between obesity and chronic pain, especially in the back and in the legs/feet. Age was not associated with chronic pain. Recently, a number of epidemiological investigations of chronic pain have been published in the general population (Lamerato et al., 2016; Walters et al., 2017). To the best of our knowledge, there has been no epidemiological investigation on chronic pain among the elderly in mainland China. One study from Taiwan among 219 elderly adults found that 42.0% of the participants had suffered from chronic pain (Yu, Tang, Yeh, Kuo, & Yu, 2011). Another study from Hong Kong among 173 older adults showed that 90.8% of the participants had suffered from pain in the previous three months (Tse, Wan, & Wong, 2013). The differences in prevalence can likely be explained by the different definitions of chronic pain and the small sample size. We used the IASP definition of chronic pain in a larger sample study, which has been proven to be widely used in Asian countries (Mohamed Zaki & Hairi, 2015). In addition, the result (49.8%) ranged between those of the two studies. This finding suggests that the prevalence of chronic pain is high among the Chinese elderly. Compared with other countries, the elderly from Singapore (19.5%), Canada (31.2%), and Sweden (38.5%) had a lower prevalence of chronic pain (Larsson, Hansson, Sundquist, & Jakobsson, 2017; Rustøen et al., 2005; Satghare et al., 2016). This difference may be determined by the different demographic characteristics of the population and different medical conditions. For example, 46.7% of our participants came from rural areas, where there were more chronic pain patients than in urban areas. Patterns of pain locations in this study are in line with results from previous studies (McCarthy et al., 2009). The most common location of chronic pain among the elderly was in the legs/feet. In China, we found that many older adults were overburdened with labor when they were young. In addition, they did not pay attention to keeping warm due to poor health consciousness and poor economic conditions in that time. These factors may be the cause of the higher incidence of pain in the
< 0.001
< 0.001 4308
2413 (56.0)
1895 (44.0)
1121
448 (40.0)
673 (60.0)
1095
413 (37.7)
682 (62.3)
23.76 ± 3.27 3.33 ± 2.90 24.13 ± 5.79
23.51 ± 3.11 2.93 ± 2.66 24.97 ± 5.69
24.00 ± 3.40 3.74 ± 3.07 23.29 ± 5.77
< 0.001 < 0.001 < 0.001
p-value obtained using χ2 tests and t-tests.
locations with the highest prevalence of pain were the legs/feet (25.5%), back (23.2%), and neck/shoulder (14.6%). The mean score of NRS was 3.60 ± 1.76 for chronic pain. The highest scores of locations were the legs/feet (3.61 ± 1.75), back (3.50 ± 1.76), and abdomen/ pelvis (3.49 ± 2.00). Table 1 and Table 2 present the characteristics of chronic pain.
3.3. Chronic pain, age, and obesity in univariate analyses 3.3.1. Chronic pain and age in univariate analyses There was a significant association between chronic pain and age in univariate analysis (p < .05). The prevalence of chronic pain was 47.8%, 51.2%, and 52.2% in those aged 60–69 years, 70–79 years and ≥80 years, respectively. Table 2 shows the associations between chronic pain locations and age. The significant associations between chronic pain and obesity only existed in the neck/shoulder and in the legs/feet (p < .05).
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Table 2 Characteristics of chronic pain according to age. Chronic pain location
Chronic pain (total) No Yes Head No Yes Face No Yes Neck/shoulder No Yes Back No Yes Arms/hands No Yes Legs/feet No Yes Chest No Yes Abdomen/pelvis No Yes
NRS (0–10)
N
p-value*
Age (years) 60–69 (%)
70–79 (%)
≥80 (%)
3274 3250
1508 (52.2) 1379 (47.8)
1388 (48.8) 1459 (51.2)
378 (47.8) 412 (52.2)
6146 378
2731 (94.6) 156 (5.4)
2680 (94.1) 167 (5.9)
735 (93.0) 55 (7.0)
6498 26
2872 (99.5) 15 (0.5)
2839 (99.7) 8 (0.3)
787 (99.6) 3 (0.4)
5569 955
2430 (84.2) 457 (15.8)
2447 (86.0) 400 (14.0)
692 (87.6) 98 (12.4)
5013 1511
2226 (77.1) 661 (22.9)
2186 (76.8) 661 (23.2)
601 (76.1) 189 (23.9)
6136 388
2714 (94.0) 173 (6.0)
2680 (94.1) 167 (5.9)
742 (93.9) 48 (6.1)
4858 1666
2290 (79.3) 597 (20.7)
2020 (71.0) 827 (29.0)
548 (69.4) 242 (30.6)
6380 144
2384 (98.2) 53 (1.8)
2780 (97.6) 67 (2.4)
766 (97.0) 24 (3.0)
6376 148
2826 (97.9) 61 (2.1)
2783 (97.8) 64 (2.2)
767 (97.1) 23 (2.9)
3.60 ± 1.76
0.012
2.86 ± 1.73
0.246
2.75 ± 1.65
0.356
3.39 ± 1.64
0.027
3.50 ± 1.76
0.828
3.21 ± 1.53
0.967
3.61 ± 1.75
< 0.001
3.09 ± 1.58
0.098
3.49 ± 2.00
0.408
* p-value obtained using χ2 tests. Table 3 Characteristics of chronic pain according to BMI. Chronic pain location
Chronic pain (total) No Yes Head No Yes Face No Yes Neck/shoulder No Yes Back No Yes Arms/hands No Yes Legs/feet No Yes Chest No Yes Abdomen/pelvis No Yes
p-value*
BMI Underweight (%)
Normal (%)
Overweight (%)
Obesity (%)
162 (53.6) 140 (46.4)
1735 (53.1) 1531 (46.9)
1139 (48.5) 1210 (51.5)
238 (39.2) 369 (60.8)
290 (96.0) 12 (4.0)
3070 (94.0) 196 (6.0)
2207 (94.0) 142 (6.0)
579 (95.4) 28 (4.6)
299 (99.0) 3 (1.0)
3251 (99.5) 15 (0.5)
2344 (99.8) 5 (0.2)
604 (99.5) 3 (0.5)
259 (85.8) 43 (14.2)
2815 (86.2) 451 (13.8)
1970 (83.9) 379 (16.1)
525 (86.5) 82 (13.5)
238 (78.8) 64 (21.2)
2566 (78.6) 700 (21.4)
1763 (75.1) 586 (24.9)
446 (73.5) 161 (26.5)
283 (93.7) 19 (6.3)
3084 (94.4) 182 (5.6)
2191 (93.3) 158 (6.7)
578 (95.2) 29 (4.8)
237 (78.5) 65 (21.5)
2530 (77.5) 736 (22.5)
1728 (73.6) 621 (26.4)
363 (59.8) 244 (40.2)
292 (96.7) 10 (3.3)
3197 (97.9) 69 (2.1)
2298 (97.8) 51 (2.2)
593 (97.7) 14 (2.3)
290 (96.0) 12 (4.0)
3139 (97.8) 73 (2.2)
2304 (98.1) 45 (1.9)
589 (97.0) 18 (3.0)
< 0.001
0.271
0.106
0.083
0.003
0.178
< 0.001
0.597
0.085
* p-value obtained using χ2 tests.
2011). Age changes in chronic pain have attracted researchers’ interest for many years. A recent systematic review showed that there is a loss of pain sensitivity in the lower pain range, which is indicated by an
legs/feet. Pain intensity is an important characteristic of pain. In this study, we selected an NRS to assess pain intensity, which had a mean score of 3.60 ± 1.76. This result is similar to previous results among the elderly in Sweden and China (Jakobsson & Larsson, 2014; Yu et al., 15
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Table 4 (continued)
Table 4 Binary logistic regression analyses of chronic pain in the rural Chinese elderly.
Variables Variables
Model 1a
Model 2b
Model 3c
OR (95%CI)
OR (95%CI)
OR (95%CI)
Model 1a
Model 2b
Model 3c
OR (95%CI)
OR (95%CI)
OR (95%CI)
MMSE Chronic pain (total) BMI Normal Underweight Overweight Obesity Age 60–69 70–79 ≥80
1.000 0.950 (0.749–1.205) 1.221 (1.097–1.358)** 1.795 (1.503–2.143)**
1.000 0.839 (0.653–1.077) 1.294 (1.156–1.448)** 1.866 (1.548–2.250)**
1.000 0.877 (0.679–1.132) 1.234 (1.100–1.384)** 1.715 (1.418–2.073)**
1.000 1.175 (1.059–1.304)** 1.262 (1.076–1.480)**
1.000 1.126 (1.005–1.260)* 1.137 (0.953–1.356)
1.000 1.042 (0.926–1.171) 1.032 (0.853–1.248)
1.000 1.983 (1.768–2.224)
1.000 2.003 (1.754–2.286)**
Sex Men Women Education Illiterate Primary Secondary or above Marital status Single Married Living alone No Yes Monthly income (yuan) < 200 200–1000 > 1000 Area Rural Urban Current smoking No Yes Current alcohol consumption No Yes Chronic diseases No Yes ADL Normal function Decline in function Significant dysfunction Depression No Yes
**
1.012 (0.999–1.025)
*p < .05, **p < .01. a Not adjusted. b Adjusted for sex, education, marital status, living status, monthly income, and area. c Adjusted for sex, education, marital status, living status, monthly income, area, current smoking, current alcohol consumption, chronic diseases, depression, ADL, and MMSE. Table 5 Binary logistic regression analyses of chronic pain locations in the rural Chinese elderly. Variables
OR
95%CI
p-value*
Head/face Normal Underweight Overweight Obesity
1.000 0.617 0.992 0.773
– 0.337–1.128 0.792–1.242 0.521–1.149
– 0.116 0.942 0.203
Neck/shoulder Normal Underweight Overweight Obesity
1.000 1.059 1.160 0.862
– 0.749–1.497 0.996–1.351 0.665–1.117
– 0.747 0.056 0.260
Back Normal Underweight Overweight Obesity
1.000 0.948 1.215 1.235
– 0.703–1.278 1.066–1.384 1.004–1.519
– 0.726 0.004 0.046
1.000 0.930 (0.812–1.066) 1.016 (0.872–1.184)
1.000 0.903 (0.780–1.045) 0.955 (0.806–1.131)
1.000 0.974 (0.823–1.154)
1.000 0.965 (0.812–1.147)
1.000 1.139 (0.943–1.375)
1.000 1.092 (0.901–1.324)
Arms/hands Normal Underweight Overweight Obesity
1.000 1.106 1.237 0.769
– 0.672–1.818 0.988–1.550 0.510–1.158
– 0.692 0.064 0.209
1.000 0.815 (0.698–0.952)* 0.750 (0.627–0.896)**
1.000 0.837 (0.715–0.981)* 0.783 (0.651–0.942)**
Legs/feet Normal Underweight Overweight Obesity
1.000 0.794 1.295 2.425
– 0.588–1.074 1.137–1.476 1.996–2.946
– 0.134 < 0.001 < 0.001
1.000 0.396 (0.342–0.459)**
1.000 0.416 (0.357–0.485)**
Chest Normal Underweight Overweight Obesity
1.000 1.452 1.060 1.055
– 0.730–2.888 0.731–1.536 0.574–1.941
– 0.287 0.760 0.863
Abdomen/pelvis Normal Underweight Overweight Obesity
1.000 1.711 0.858 1.343
– 0.908–3.223 0.587–1.254 0.789–2.286
– 0.097 0.429 0.287
1.000 0.973 (0.819–1.156) 1.000 1.004 (0.844–1.194)
* Adjusted for sex, age, education, marital status, living status, monthly income, area, current smoking, current alcohol consumption, chronic diseases, depression, ADL, and MMSE.
1.000 2.271 (1.982–2.601)**
increase in the pain thresholds of older adults (Lautenbacher, Peters, Heesen, Scheel, & Kunz, 2017). The perception of pain decreased gradually with age. Consistent with previous studies, our result of pain intensity was lower than the results from adolescents and young adults (de la Vega et al., 2016; Harifi et al., 2013). From another point of view, many researchers believe that chronic pain is more common in the elderly. Our univariate analysis also showed that the prevalence of chronic pain increases with age among the elderly. However, age was not associated with chronic pain after adjusting for risk factors. The prevalence of diseases and disabilities increases with age among the elderly. This increase in prevalence may be due to socio-demographic characteristics, such as education, monthly income and diseases
1.000 1.087 (0.929–1.272) 1.194 (1.002–1.422)* 1.000 1.508 (1.255–1.812)**
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and the Major Program of the Social Science Foundation of Anhui Province, China, 2014 (SK2014ZD037). The authors would like to thank the elderly adults who participated in the study. In addition, the authors would also like to thank the Ma’anshan Centers for Disease Control and Prevention, Anhui, China, for providing help with this study.
(Satghare et al., 2016). The disease burden of high BMI has been increasing globally since 1980 (GBD Obesity Collaborators, 2017). Chronic pain was considered to be associated with obesity in recent decades (Heim, Snijder, Deeg, Seidell, & Visser, 2008; Tanamas et al., 2012). Our study showed the same findings. Compared to normal weight group, the overweight group reported 23.3% higher rates of chronic pain and the obesity group reported 71.7% higher rates of chronic pain among the Chinese elderly. Unlike previous studies, we explored not only the association between obesity and chronic pain but also the associations between obesity and chronic pain locations. The associations between chronic pain and obesity were only in the legs/feet and in the back. The mechanisms underlying the association between chronic pain and obesity remain unclear. Some reports implicated that inflammation mechanisms were possible explanations for this association. Adipose tissue functions as a key endocrine organ by releasing multiple bioactive substances that have pro-inflammatory activities (Ouchi, Parker, Lugus, & Walsh, 2011). Adipocytokine leptin emerges as a potential obesity-related systemic factor that could push intra-articular balance towards inflammation and destruction in the pathogenesis of osteoarthritis (OA) (Vuolteenaho, Koskinen, & Moilanen, 2014). One study showed high levels of C-reactive protein (CRP), an inflammation marker, may increase the odds of reporting low back pain (LBP), especially in obesity participants (Briggs, Givens, Schmitt, & Taylor, 2013). Mechanical and structural factors remain important mechanisms in the obesity and pain association (Narouze & Souzdalnitski, 2015). For example, an inter-relationship was identified between obesity, lumbar disc height, and recent pain, suggesting that structural changes have a role in back pain and may in part explain the association between obesity and back pain (Urquhart et al., 2014). Previous studies also demonstrate that familial, socio-demographic and psychosocial factors may be partially explain the association (Wright et al., 2010). One of our strengths was that we conducted a large sample survey of chronic pain and obesity in the elderly. To the best of our knowledge, this is the first epidemiological survey on this topic among older adults in mainland China. Another strength of this survey was that we explored not only the association between total chronic pain and obesity but also the association between chronic pain locations and obesity. In addition, the associations were proven to be different. There are also limitations in this study. First, the treatment of pain was not surveyed in this study. Some participants did not report pain due to having had treatment for pain, which would lead to an underestimation of the true associations. Second, the design method of this study was a cross-sectional approach. It restricted the ability to determine the time course between chronic pain and obesity. Therefore, we cannot determine the causal relationship between the two variables. Third, this survey was limited to one municipality of Anhui Province in China; thus, caution should be taken when generalizing the results to other populations.
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Chronic pain in older adults: Prevalence, incidence, and risk factors. Scandinavian Journal of Rheumatology, 46(4), 317–325. Lautenbacher, S., Peters, J. H., Heesen, M., Scheel, J., & Kunz, M. (2017). Age changes in pain perception: A systematic-review and meta-analysis of age effects on pain and tolerance thresholds. Neuroscience and Biobehavioral Reviews, 2017(75), 104–113. Malta, D. C., Oliveira, M. M., Andrade, S. S. C. A., Caiaffa, W. T., Souza, M. F. M., & Bernal, R. T. I. (2017). Factors associated with chronic back pain in adults in Brazil. Revista de Saude Publica, 51(Suppl. 1), 9s. http://dx.doi.org/10.1590/S1518-8787. 2017051000052. Marcus, D. A. (2004). Obesity and the impact of chronic pain. Clinical Journal of Pain, 20(3), 186–191. McCarthy, L. H., Bigal, M. E., Katz, M., Derby, C., & Lipton, R. B. (2009). Chronic pain and obesity in elderly people: Results from the Einstein aging study. Journal of the American Geriatrics Society, 57(1), 115–119. Mohamed Zaki, L. R., & Hairi, N. N. (2015). A systematic review of the prevalence and measurement of chronic pain in asian adults. Pain Management Nursing, 16(3), 440–452. Narouze, S., & Souzdalnitski, D. (2015). Obesity and chronic pain: Systematic review of prevalence and implications for pain practice. Regional Anesthesia and Pain Medicine, 40(2), 91–111. National Bureau of Statistics of China (2016). China statistical yearbook. [Accessed 17 September, 2019] http://www.stats.gov.cn/tjsj/ndsj/2016/indexch.htm. Ouchi, N., Parker, J. L., Lugus, J. J., & Walsh, K. (2011). Adipokines in inflammation and metabolic disease. Nature Reviews Immunology, 11(2), 85–97. Patel, K. V., Guralnik, J. M., Dansie, E. J., & Turk, D. C. (2013). Prevalence and impact of pain among older adults in the United States: Findings from the 2011 National Health and Aging Trends Study. Pain, 154(12), 2649–2657. Rustøen, T., Wahl, A. K., Hanestad, B. R., Lerdal, A., Paul, S., & Miaskowski, C. (2005). Age and the experience of chronic pain: Differences in health and quality of life among younger, middle-aged, and older adults. Clinical Journal of Pain, 21(6), 513–523. Satghare, P., Chong, S. A., Vaingankar, J., Picco, L., Abdin, E., Chua, B. Y., et al. (2016). Prevalence and correlates of pain in people aged 60 years and above in Singapore: Results from the WiSE study. Pain Research & Management, 2016, 7852397. http://dx. doi.org/10.1155/2016/7852397.
5. Conclusion Our study showed that nearly 1 in 2 older adults were suffering from chronic pain in China. It is necessary to pay attention to the prevention and management of chronic pain. Obesity is independently associated with chronic pain in the elderly. This finding may provide an important clue on how to best manage chronic pain. Conflict of interest statement None. Acknowledgements This study was supported by grants from the Talent Program of the Higher Education Revitalization Plan of Anhui Province, China, 2013 17
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Vuolteenaho, K., Koskinen, A., & Moilanen, E. (2014). Leptin – A link between obesity and osteoarthritis. Applications for prevention and treatment. Basic & Clinical Pharmacology & Toxicology, 114(1), 103–108. Walters, J. L., Baxter, K., Chapman, H., Jackson, T., Sethuramachandran, A., Couldridge, M., et al. (2017). Chronic pain and associated factors in India and Nepal: A pilot study of the vanderbilt global pain survey. Anesthesia and Analgesia, 10. http://dx.doi.org/ 10.1213/ANE.0000000000002360. Woo, J., Ho, S. C., Lau, J., Yuen, Y. K., Chiu, H., Lee, H. C., et al. (1994). The prevalence of depressive symptoms and predisposing factors in an elderly Chinese population. Acta Psychiatrica Scandinavica, 89(1), 8–13. Wright, L. J., Schur, E., Noonan, C., Ahumada, S., Buchwald, D., & Afari, N. (2010). Chronic pain, overweight, and obesity: Findings from a community-based twin registry. Journal of Pain, 11(7), 628–635. Yu, H. Y., Tang, F. I., Yeh, M. C., Kuo, B. I., & Yu, S. (2011). Use, perceived effectiveness, and gender differences of pain relief strategies among the community-dwelling elderly in Taiwan. Pain Management Nursing, 12(1), 41–49. Zhou, B. F. (2002). Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults–study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomedical and Environmental Sciences, 15(1), 83–96 [Cooperative Meta-Analysis Group of the Working Group on Obesity in China].
Stone, A. A., & Broderick, J. E. (2012). Obesity and pain are associated in the United States. Obesity (Silver Spring), 20(7), 1491–1495. Su, P. Y., Hao, J. H., Xiong, L. M., Yu, D. D., Cao, Y. T., Fang, Y., et al. (2011). The prevalence and influencing factors of abuse and negligence against elderly in rural areas of Anhui. Chinese Journal of Epidemiology, 32(2), 110–115 [in Chinese]. Takura, T., Ushida, T., Kanchiku, T., Ebata, N., Fujii, K., DiBonaventura, M. D., et al. (2015). The societal burden of chronic pain in Japan: An internet survey. Journal of Orthopaedic Science, 20(4), 750–760. Tanamas, S. K., Wluka, A. E., Berry, P., Menz, H. B., Strauss, B. J., Davies-Tuck, M., et al. (2012). Relationship between obesity and foot pain and its association with fat mass, fat distribution, and muscle mass. Arthritis Care & Research (Hoboken), 64(2), 262–268. Tsang, A., Von Korff, M., Lee, S., Alonso, J., Karam, E., Angermeyer, M. C., et al. (2008). Common chronic pain conditions in developed and developing countries: Gender and age differences and comorbidity with depression-anxiety disorders? Journal of Pain, 9(10), 883–891. Tse, M., Wan, V. T., & Wong, A. M. (2013). Pain and pain-related situations surrounding community-dwelling older persons. Journal of Clinical Nursing, 22(13–14), 1870–1879. Urquhart, D. M., Kurniadi, I., Triangto, K., Wang, Y., Wluka, A. E., O’Sullivan, R., et al. (2014). Obesity is associated with reduced disc height in the lumbar spine but not at the lumbosacral junction. Spine, 39(16), E962–E966 [Phila Pa 1976].
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Contents lists available at ScienceDirect
Archives of Gerontology and Geriatrics journal homepage: www.elsevier.com/locate/archger
Chronic pain and its association with obesity among older adults in China a,b
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Jie Li , Jian Chen , Qirong Qin , Dongdong Zhao , Bao Dong , Qiongqiong Ren , Dandan Yu , ⁎ Peng Bia,d, Yehuan Suna,e, a
Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China School of Public Health, Wannan Medical College, Wuhu, Anhui, China Ma’anshan Center for Disease Control and Prevention, Ma’anshan, Anhui, China d School of Public Health, The University of Adelaide, Adelaide, South Australia, Australia e Center for Evidence-Based Practice, Anhui Medical University, Hefei, Anhui, China b c
A R T I C L E I N F O
A B S T R A C T
Keywords: China Chronic pain Cross-sectional survey Elderly Obesity
Objectives: There is a paucity of epidemiological data on chronic pain and obesity among older adults. This study attempted to present the characterization of chronic pain and its association with obesity among the Chinese elderly. Methods: A cross-sectional survey was undertaken among 6524 elderly individuals aged ≥60 years in China. Chronic pain was identified by self-reports based on the definition from the International Association for the Study of Pain (IASP). Body Mass Index (BMI) was measured to assess obesity. Binary logistic regression analysis was performed to explore the association between obesity and chronic pain. Results: The prevalence of chronic pain was 49.8%. The legs/feet (25.5%), back (23.2%), and neck/shoulder (14.6%) were the most salient locations for chronic pain. Compared with normal weight, subjects with overweight (OR = 1.234, 95%CI = 1.100–1.384) and obesity (OR = 1.715, 95%CI = 1.418–2.073) were considerably more likely to have chronic pain after adjusting for covariates (p < .05). Age was not significantly associated with chronic pain (p > .05). Further analyses revealed that the associations between chronic pain and obesity were restricted to the legs/feet and back. Conclusion: Chronic pain is common among older adults in China. Understanding the role of obesity in chronic pain is important for preventing and treating chronic pain.
1. Introduction According to the International Association for the Study of Pain (IASP), pain is defined as an unpleasant sensory and emotional experience associated with actual or potential tissue damage or described in terms of such damage, and it is classified as chronic pain if it persists for longer than 3 months (IASP, 1986). Surveys showed that the prevalence of chronic pain was 37.3% in developed countries and 41.1% in developing countries among the general adult population (Tsang et al., 2008). Age is often considered to be associated with chronic pain (Chen et al., 2016; Lamerato et al., 2016). A systematic review in Asian adults showed that the prevalence of chronic pain among adults aged 18 years and above varied from 7.1% to 61%, while the prevalence varied from 42% to 90.8% among the Asian geriatric population (3 studies) (Mohamed Zaki & Hairi, 2015). However, some studies showed that the prevalence of chronic pain was not associated with age in elderly people (Satghare et al., 2016; Patel, Guralnik, Dansie, & Turk, 2013).
⁎
Researchers have not reached a consistent conclusion that there is a flat or increasing prevalence of chronic pain among older adults. The age pattern of chronic pain in the elderly is not well established due to very few studies in older adults. Since 1980, the prevalence of obesity has doubled in more than 70 countries and has continuously increased in most countries (GBD Obesity Collaborators, 2017). The disease burden related to high BMI has increased accordingly. A number of studies over the last few decades have examined the association between obesity and pain (Malta et al., 2017; Tanamas et al., 2012). A survey of 1062271 individuals in the United States showed that BMI and pain are positively correlated: the overweight group reported 20% higher rates of pain than the lownormal group, 68% higher for the obese Ⅰ group, 136% higher for the obese Ⅱ group, and 254% higher for the obese Ⅲ group (Stone & Broderick, 2012). The association between chronic pain and obesity is important, since obesity is also associated with increased pain-related disability and reduced physical functioning (Marcus, 2004).
Corresponding author at: Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan road, Hefei, Anhui 230032, China. E-mail address: [email protected] (Y. Sun).
https://doi.org/10.1016/j.archger.2018.01.009 Received 7 November 2017; Received in revised form 24 January 2018; Accepted 25 January 2018 0167-4943/ © 2018 Elsevier B.V. All rights reserved.
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four groups: underweight (< 18.5 kg/m2), normal weight (18.5–24.0 kg/m2), overweight (24.0–28.0 kg/m2) and obese (≥28.0 kg/m2), based on Chinese standards of BMI (Zhou, 2002).
Nevertheless, the association between chronic pain and obesity has not been fully studied. Data on the association between chronic pain and obesity in the elderly population is limited. Given that older adults have a different state of health, such as decreased metabolic capacity and decreased sensitivity to pain, it is necessary to clarify the association between chronic pain and obesity in the elderly population. The increasing ratio trend of the aging population is well underway, while the overall global population is declining (Fuster, 2017). Within the context of aging, China entered the stage of population aging in 2000, and the proportion of aging increased year by year. The proportion of people aged 60 and above was 16.14% in 2015 (National Bureau of Statistics of China, 2016). Studies showed that chronic pain may have significant impacts on health and well-being, such as daily activity impairments, and on lower quality of life (Inoue et al., 2015; Takura et al., 2015). It is of great significance for healthy aging to prevent chronic pain. Therefore, the aims of this study were to investigate the following: (1) the prevalence and age pattern of chronic pain among the Chinese elderly; and (2) the associations between chronic pain and obesity.
2.2.3. Socio-demographic (or other) variables The following socio-demographic variables were included in this survey: sex (men, women), age (60–69, 70–79, ≥80 years), education (illiterate, primary, secondary or above), marital status (single, including never married, divorced, widowed; married), living alone (no, yes), monthly income (< 200 RMB, 200–1000 RMB, > 1000 RMB), area (rural, urban), current smoking (no, yes), current alcohol consumption (no, yes), chronic diseases (no; yes, including hypertension, diabetes mellitus, coronary heart disease, arrhythmia, stoke, chronic lung diseases, chronic stomach diseases, cancer, cataract, glaucoma, cholecystitis, and nephritis based on final diagnosis from medical institutions). Activity of Daily Living (ADL) was measured using a 14-item scale (Physical Self-Maintenance Scale and Instrumental Activity of Daily Living). The sum scores ranged from 4 to 64. It was classified into three categories: normal function (sum score < 16), decline in function (sum score: 16–21), and significant dysfunction (sum score ≥22 or two or more items scores greater than three) (Su et al., 2011). Depression was measured by the GDS-15 (15-item Geriatric Depression Scale). This scale contains 15 items (score: 0–15), with higher scores indicating poorer depressive symptoms. A cutoff score of ≥8 is defined as depression (Woo et al., 1994). Cognitive assessment was performed by Mini-Mental State Examination (MMSE), which is widely used to assess cognitive function (Folstein, Folstein, & McHugh, 1975). It contains 30 items, with scores ranging from 0 to 30. A higher score corresponds to a better cognitive state.
2. Methods 2.1. Participants This study was conducted in Ma’anshan, a municipality located in eastern Anhui Province, China. The study participants were derived from two stages. First, we investigated the rural elderly population using a cluster random sample approach. We contacted 27 rural villages in Dangtu County of Ma’anshan City in September to October 2016. Second, we investigated the urban elderly in December 2016 to March 2017. Three community health centers were randomly selected. To improve the response rate, we presented a small incentive gift to the participants. Furthermore, we provided free physical examination services for them, such as measuring blood pressure to promote participation. In addition, the Ma’anshan Centers for Disease Control also provided many forms of support for this investigation. Nearly half of the older adults were illiterate in this investigation. Thus, we explained the contents of the questionnaire to the participants with the help of graduate students and other local older adults. A total of 6881 elderly aged 60 years or above were included. Face-to-face interviews and physical examinations were conducted. After the completion of the questionnaire, the investigators needed to check the integrity and logic of the questionnaire. Eventually, 6524 older adults were successfully interviewed. The overall response rate was 94.8%. Three weeks later, 200 elderly people were selected to retest the content of the questionnaire. The Ethical approval was granted by the ethics committee of Anhui Medical University.
2.3. Statistical analysis All statistical analyses for this study were conducted using SPSS 16.0 (SPSS for Windows, Version 16.0, Chicago, SPSS Inc.). A pvalue < .05 was designated as showing a significant difference. The differences between chronic pain and characteristics of the elderly were examined using χ2 tests and t-tests. The differences between chronic pain locations and BMI and age were compared using χ2 tests. Binary logistic regression analyses were applied to assess the associations between chronic pain and obesity. The dependent variable was chronic pain (no = 0, yes = 1). 3. Results 3.1. General characteristics of subjects The study population consisted of 3685 women (56.5%) and 2839 men (43.5%). Participants were on average 71.09 ± 6.73 years of age, and 44.3% of them were 60–69 years. Nearly half of them (48.0%) were illiterate. Most participants were married (76.6%) and living with a spouse or other family members (84.1%). A total of 3048 (46.7%) older adults came from rural areas, and 3476 (53.3%) older adults came from urban areas. Table 1 shows the descriptive statistics of the older adults.
2.2. Measures 2.2.1. Chronic pain In the present study, chronic pain was defined using the IASP definition. First, subjects were asked “Did you have pain in the past 6 months lasting ≥3 months in duration?” If yes, 8 locations (head, face, neck and shoulder, back, arms and hands, legs and feet, chest, and abdomen and pelvis) were given (McCarthy, Bigal, Katz, Derby, & Lipton, 2009). One or more locations could be selected according to the actual condition. Subjects were then asked to rate their pain intensity. Pain intensity was assessed using a numerical rating scale (NRS) score (Gianni et al., 2010). It is scored between 0 (no pain) and 10 (worst possible pain). The total pain intensity was identified as the highest pain intensity of the locations.
3.2. Characteristics of chronic pain The retest reliability of chronic pain was 0.89. The prevalence of chronic pain was 49.8% (3250) among the older adults in this sample. Compared with the population with no pain, the population with chronic pain had a higher proportion of women, older individuals, those with lower levels of education, those who were single in terms of marital status, those who had lower incomes, and those who lived in rural areas (p < .05). Among all the chronic pain participants, a total of 1963 (60.4%) participants reported pain in one location, and 1287 (39.6%) participants reported pain in two or more locations. The
2.2.2. Obesity Weight and height were measured during the face-to-face interview. BMI is calculated as weight (kg)/[height (m)]2. It is categorized into 13
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3.3.2. Chronic pain and obesity in univariate analyses There was a significant association between chronic pain and BMI (p < .05). A total of 60.8% of the participants with obesity had chronic pain, while 46.9% of the participants with normal weight had chronic pain. Table 3 presents the associations between chronic pain locations and BMI category. The significant associations existed in the back and in the legs/feet (p < .05). No significant results were found between BMI category and chronic pain in other locations.
Table 1 Characteristics of the elderly according to chronic pain (n = 6524). Characteristics
Sex Men (%) Women (%) Age (years) 60–69 (%) 70–79 (%) ≥80 (%) Education Illiterate (%) Primary (%) Secondary or above (%) Marital status Single (%) Married (%) Living alone No (%) Yes (%) Monthly income (yuan) < 200 (%) 200–1000 (%) > 1000 (%) Area Rural (%) Urban (%) Current smoking No (%) Yes (%) Current alcohol consumption No (%) Yes (%) Chronic diseases No (%) Yes (%) Activity of Daily Living Normal function (%) Decline in function (%) Significant dysfunction (%) BMI (kg/m2) Depression (score) MMSE (score) *
N
p-value*
Chronic pain No
Yes
2839 3685
1714 (60.4) 1560 (42.3)
1125 (39.6) 2125 (57.7)
2887 2847 790
1508 (52.2) 1388 (48.8) 378 (47.8)
1379 (47.8) 1459 (51.2) 412 (52.2)
3122 1635 1767
1273 (40.8) 902 (55.2) 1099 (62.2)
1849 (59.2) 733 (44.8) 668 (37.8)
1524 5000
626 (41.1) 2648 (53.0)
898 (58.9) 2352 (47.0)
5489 1035
2862 (52.1) 412 (39.8)
2627 (47.9) 623 (60.2)
< 0.001
0.012
3.4. Binary logistic regression analyses of chronic pain The results of binary logistic regression analyses between chronic pain and obesity are listed in Table 4. Model 1 showed that BMI and age were associated with chronic pain. After including socio-demographic and other variables in model 3, the results showed that women, rural participants, and those with lower monthly incomes, chronic diseases, depression, and significant dysfunction of ADL were more likely to report chronic pain. However, no association was found between chronic pain and age. Overweight (OR = 1.224, 95%CI = 1.079–1.389) and obesity (OR = 1.697, 95%CI = 1.389–2.073) were significantly associated with chronic pain in model 3. The results of binary logistic regression analyses between chronic pain locations and obesity are listed in Table 5. Chronic pain in the back and legs/feet was associated with obesity, after adjusting for sex, age, education, marital status, living status, monthly income, area, current smoking, current alcohol consumption, chronic diseases, depression, ADL, and MMSE.
< 0.001
< 0.001
< 0.001
< 0.001 1398 1676 3450
474 (33.9) 713 (42.5) 2087 (60.5)
924 (66.1) 963 (57.5) 1363 (39.5)
3048 3476
1115 (36.6) 2159 (62.1)
1933 (63.4) 1317 (37.9)
5604 920
2737 (48.8) 537 (58.4)
2867 (51.2) 383 (41.6)
< 0.001
< 0.001
4. Discussion < 0.001
5676 848
2794 (49.2) 480 (56.6)
2882 (50.8) 368 (43.4)
1322 5202
904 (68.4) 2370 (45.6)
418 (31.6) 2832 (54.4)
This cross-sectional epidemiological study showed the characteristics of chronic pain and its associations with obesity among older adults in China. Nearly half of the participants had chronic pain. The results revealed a significant association between obesity and chronic pain, especially in the back and in the legs/feet. Age was not associated with chronic pain. Recently, a number of epidemiological investigations of chronic pain have been published in the general population (Lamerato et al., 2016; Walters et al., 2017). To the best of our knowledge, there has been no epidemiological investigation on chronic pain among the elderly in mainland China. One study from Taiwan among 219 elderly adults found that 42.0% of the participants had suffered from chronic pain (Yu, Tang, Yeh, Kuo, & Yu, 2011). Another study from Hong Kong among 173 older adults showed that 90.8% of the participants had suffered from pain in the previous three months (Tse, Wan, & Wong, 2013). The differences in prevalence can likely be explained by the different definitions of chronic pain and the small sample size. We used the IASP definition of chronic pain in a larger sample study, which has been proven to be widely used in Asian countries (Mohamed Zaki & Hairi, 2015). In addition, the result (49.8%) ranged between those of the two studies. This finding suggests that the prevalence of chronic pain is high among the Chinese elderly. Compared with other countries, the elderly from Singapore (19.5%), Canada (31.2%), and Sweden (38.5%) had a lower prevalence of chronic pain (Larsson, Hansson, Sundquist, & Jakobsson, 2017; Rustøen et al., 2005; Satghare et al., 2016). This difference may be determined by the different demographic characteristics of the population and different medical conditions. For example, 46.7% of our participants came from rural areas, where there were more chronic pain patients than in urban areas. Patterns of pain locations in this study are in line with results from previous studies (McCarthy et al., 2009). The most common location of chronic pain among the elderly was in the legs/feet. In China, we found that many older adults were overburdened with labor when they were young. In addition, they did not pay attention to keeping warm due to poor health consciousness and poor economic conditions in that time. These factors may be the cause of the higher incidence of pain in the
< 0.001
< 0.001 4308
2413 (56.0)
1895 (44.0)
1121
448 (40.0)
673 (60.0)
1095
413 (37.7)
682 (62.3)
23.76 ± 3.27 3.33 ± 2.90 24.13 ± 5.79
23.51 ± 3.11 2.93 ± 2.66 24.97 ± 5.69
24.00 ± 3.40 3.74 ± 3.07 23.29 ± 5.77
< 0.001 < 0.001 < 0.001
p-value obtained using χ2 tests and t-tests.
locations with the highest prevalence of pain were the legs/feet (25.5%), back (23.2%), and neck/shoulder (14.6%). The mean score of NRS was 3.60 ± 1.76 for chronic pain. The highest scores of locations were the legs/feet (3.61 ± 1.75), back (3.50 ± 1.76), and abdomen/ pelvis (3.49 ± 2.00). Table 1 and Table 2 present the characteristics of chronic pain.
3.3. Chronic pain, age, and obesity in univariate analyses 3.3.1. Chronic pain and age in univariate analyses There was a significant association between chronic pain and age in univariate analysis (p < .05). The prevalence of chronic pain was 47.8%, 51.2%, and 52.2% in those aged 60–69 years, 70–79 years and ≥80 years, respectively. Table 2 shows the associations between chronic pain locations and age. The significant associations between chronic pain and obesity only existed in the neck/shoulder and in the legs/feet (p < .05).
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Table 2 Characteristics of chronic pain according to age. Chronic pain location
Chronic pain (total) No Yes Head No Yes Face No Yes Neck/shoulder No Yes Back No Yes Arms/hands No Yes Legs/feet No Yes Chest No Yes Abdomen/pelvis No Yes
NRS (0–10)
N
p-value*
Age (years) 60–69 (%)
70–79 (%)
≥80 (%)
3274 3250
1508 (52.2) 1379 (47.8)
1388 (48.8) 1459 (51.2)
378 (47.8) 412 (52.2)
6146 378
2731 (94.6) 156 (5.4)
2680 (94.1) 167 (5.9)
735 (93.0) 55 (7.0)
6498 26
2872 (99.5) 15 (0.5)
2839 (99.7) 8 (0.3)
787 (99.6) 3 (0.4)
5569 955
2430 (84.2) 457 (15.8)
2447 (86.0) 400 (14.0)
692 (87.6) 98 (12.4)
5013 1511
2226 (77.1) 661 (22.9)
2186 (76.8) 661 (23.2)
601 (76.1) 189 (23.9)
6136 388
2714 (94.0) 173 (6.0)
2680 (94.1) 167 (5.9)
742 (93.9) 48 (6.1)
4858 1666
2290 (79.3) 597 (20.7)
2020 (71.0) 827 (29.0)
548 (69.4) 242 (30.6)
6380 144
2384 (98.2) 53 (1.8)
2780 (97.6) 67 (2.4)
766 (97.0) 24 (3.0)
6376 148
2826 (97.9) 61 (2.1)
2783 (97.8) 64 (2.2)
767 (97.1) 23 (2.9)
3.60 ± 1.76
0.012
2.86 ± 1.73
0.246
2.75 ± 1.65
0.356
3.39 ± 1.64
0.027
3.50 ± 1.76
0.828
3.21 ± 1.53
0.967
3.61 ± 1.75
< 0.001
3.09 ± 1.58
0.098
3.49 ± 2.00
0.408
* p-value obtained using χ2 tests. Table 3 Characteristics of chronic pain according to BMI. Chronic pain location
Chronic pain (total) No Yes Head No Yes Face No Yes Neck/shoulder No Yes Back No Yes Arms/hands No Yes Legs/feet No Yes Chest No Yes Abdomen/pelvis No Yes
p-value*
BMI Underweight (%)
Normal (%)
Overweight (%)
Obesity (%)
162 (53.6) 140 (46.4)
1735 (53.1) 1531 (46.9)
1139 (48.5) 1210 (51.5)
238 (39.2) 369 (60.8)
290 (96.0) 12 (4.0)
3070 (94.0) 196 (6.0)
2207 (94.0) 142 (6.0)
579 (95.4) 28 (4.6)
299 (99.0) 3 (1.0)
3251 (99.5) 15 (0.5)
2344 (99.8) 5 (0.2)
604 (99.5) 3 (0.5)
259 (85.8) 43 (14.2)
2815 (86.2) 451 (13.8)
1970 (83.9) 379 (16.1)
525 (86.5) 82 (13.5)
238 (78.8) 64 (21.2)
2566 (78.6) 700 (21.4)
1763 (75.1) 586 (24.9)
446 (73.5) 161 (26.5)
283 (93.7) 19 (6.3)
3084 (94.4) 182 (5.6)
2191 (93.3) 158 (6.7)
578 (95.2) 29 (4.8)
237 (78.5) 65 (21.5)
2530 (77.5) 736 (22.5)
1728 (73.6) 621 (26.4)
363 (59.8) 244 (40.2)
292 (96.7) 10 (3.3)
3197 (97.9) 69 (2.1)
2298 (97.8) 51 (2.2)
593 (97.7) 14 (2.3)
290 (96.0) 12 (4.0)
3139 (97.8) 73 (2.2)
2304 (98.1) 45 (1.9)
589 (97.0) 18 (3.0)
< 0.001
0.271
0.106
0.083
0.003
0.178
< 0.001
0.597
0.085
* p-value obtained using χ2 tests.
2011). Age changes in chronic pain have attracted researchers’ interest for many years. A recent systematic review showed that there is a loss of pain sensitivity in the lower pain range, which is indicated by an
legs/feet. Pain intensity is an important characteristic of pain. In this study, we selected an NRS to assess pain intensity, which had a mean score of 3.60 ± 1.76. This result is similar to previous results among the elderly in Sweden and China (Jakobsson & Larsson, 2014; Yu et al., 15
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Table 4 (continued)
Table 4 Binary logistic regression analyses of chronic pain in the rural Chinese elderly.
Variables Variables
Model 1a
Model 2b
Model 3c
OR (95%CI)
OR (95%CI)
OR (95%CI)
Model 1a
Model 2b
Model 3c
OR (95%CI)
OR (95%CI)
OR (95%CI)
MMSE Chronic pain (total) BMI Normal Underweight Overweight Obesity Age 60–69 70–79 ≥80
1.000 0.950 (0.749–1.205) 1.221 (1.097–1.358)** 1.795 (1.503–2.143)**
1.000 0.839 (0.653–1.077) 1.294 (1.156–1.448)** 1.866 (1.548–2.250)**
1.000 0.877 (0.679–1.132) 1.234 (1.100–1.384)** 1.715 (1.418–2.073)**
1.000 1.175 (1.059–1.304)** 1.262 (1.076–1.480)**
1.000 1.126 (1.005–1.260)* 1.137 (0.953–1.356)
1.000 1.042 (0.926–1.171) 1.032 (0.853–1.248)
1.000 1.983 (1.768–2.224)
1.000 2.003 (1.754–2.286)**
Sex Men Women Education Illiterate Primary Secondary or above Marital status Single Married Living alone No Yes Monthly income (yuan) < 200 200–1000 > 1000 Area Rural Urban Current smoking No Yes Current alcohol consumption No Yes Chronic diseases No Yes ADL Normal function Decline in function Significant dysfunction Depression No Yes
**
1.012 (0.999–1.025)
*p < .05, **p < .01. a Not adjusted. b Adjusted for sex, education, marital status, living status, monthly income, and area. c Adjusted for sex, education, marital status, living status, monthly income, area, current smoking, current alcohol consumption, chronic diseases, depression, ADL, and MMSE. Table 5 Binary logistic regression analyses of chronic pain locations in the rural Chinese elderly. Variables
OR
95%CI
p-value*
Head/face Normal Underweight Overweight Obesity
1.000 0.617 0.992 0.773
– 0.337–1.128 0.792–1.242 0.521–1.149
– 0.116 0.942 0.203
Neck/shoulder Normal Underweight Overweight Obesity
1.000 1.059 1.160 0.862
– 0.749–1.497 0.996–1.351 0.665–1.117
– 0.747 0.056 0.260
Back Normal Underweight Overweight Obesity
1.000 0.948 1.215 1.235
– 0.703–1.278 1.066–1.384 1.004–1.519
– 0.726 0.004 0.046
1.000 0.930 (0.812–1.066) 1.016 (0.872–1.184)
1.000 0.903 (0.780–1.045) 0.955 (0.806–1.131)
1.000 0.974 (0.823–1.154)
1.000 0.965 (0.812–1.147)
1.000 1.139 (0.943–1.375)
1.000 1.092 (0.901–1.324)
Arms/hands Normal Underweight Overweight Obesity
1.000 1.106 1.237 0.769
– 0.672–1.818 0.988–1.550 0.510–1.158
– 0.692 0.064 0.209
1.000 0.815 (0.698–0.952)* 0.750 (0.627–0.896)**
1.000 0.837 (0.715–0.981)* 0.783 (0.651–0.942)**
Legs/feet Normal Underweight Overweight Obesity
1.000 0.794 1.295 2.425
– 0.588–1.074 1.137–1.476 1.996–2.946
– 0.134 < 0.001 < 0.001
1.000 0.396 (0.342–0.459)**
1.000 0.416 (0.357–0.485)**
Chest Normal Underweight Overweight Obesity
1.000 1.452 1.060 1.055
– 0.730–2.888 0.731–1.536 0.574–1.941
– 0.287 0.760 0.863
Abdomen/pelvis Normal Underweight Overweight Obesity
1.000 1.711 0.858 1.343
– 0.908–3.223 0.587–1.254 0.789–2.286
– 0.097 0.429 0.287
1.000 0.973 (0.819–1.156) 1.000 1.004 (0.844–1.194)
* Adjusted for sex, age, education, marital status, living status, monthly income, area, current smoking, current alcohol consumption, chronic diseases, depression, ADL, and MMSE.
1.000 2.271 (1.982–2.601)**
increase in the pain thresholds of older adults (Lautenbacher, Peters, Heesen, Scheel, & Kunz, 2017). The perception of pain decreased gradually with age. Consistent with previous studies, our result of pain intensity was lower than the results from adolescents and young adults (de la Vega et al., 2016; Harifi et al., 2013). From another point of view, many researchers believe that chronic pain is more common in the elderly. Our univariate analysis also showed that the prevalence of chronic pain increases with age among the elderly. However, age was not associated with chronic pain after adjusting for risk factors. The prevalence of diseases and disabilities increases with age among the elderly. This increase in prevalence may be due to socio-demographic characteristics, such as education, monthly income and diseases
1.000 1.087 (0.929–1.272) 1.194 (1.002–1.422)* 1.000 1.508 (1.255–1.812)**
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and the Major Program of the Social Science Foundation of Anhui Province, China, 2014 (SK2014ZD037). The authors would like to thank the elderly adults who participated in the study. In addition, the authors would also like to thank the Ma’anshan Centers for Disease Control and Prevention, Anhui, China, for providing help with this study.
(Satghare et al., 2016). The disease burden of high BMI has been increasing globally since 1980 (GBD Obesity Collaborators, 2017). Chronic pain was considered to be associated with obesity in recent decades (Heim, Snijder, Deeg, Seidell, & Visser, 2008; Tanamas et al., 2012). Our study showed the same findings. Compared to normal weight group, the overweight group reported 23.3% higher rates of chronic pain and the obesity group reported 71.7% higher rates of chronic pain among the Chinese elderly. Unlike previous studies, we explored not only the association between obesity and chronic pain but also the associations between obesity and chronic pain locations. The associations between chronic pain and obesity were only in the legs/feet and in the back. The mechanisms underlying the association between chronic pain and obesity remain unclear. Some reports implicated that inflammation mechanisms were possible explanations for this association. Adipose tissue functions as a key endocrine organ by releasing multiple bioactive substances that have pro-inflammatory activities (Ouchi, Parker, Lugus, & Walsh, 2011). Adipocytokine leptin emerges as a potential obesity-related systemic factor that could push intra-articular balance towards inflammation and destruction in the pathogenesis of osteoarthritis (OA) (Vuolteenaho, Koskinen, & Moilanen, 2014). One study showed high levels of C-reactive protein (CRP), an inflammation marker, may increase the odds of reporting low back pain (LBP), especially in obesity participants (Briggs, Givens, Schmitt, & Taylor, 2013). Mechanical and structural factors remain important mechanisms in the obesity and pain association (Narouze & Souzdalnitski, 2015). For example, an inter-relationship was identified between obesity, lumbar disc height, and recent pain, suggesting that structural changes have a role in back pain and may in part explain the association between obesity and back pain (Urquhart et al., 2014). Previous studies also demonstrate that familial, socio-demographic and psychosocial factors may be partially explain the association (Wright et al., 2010). One of our strengths was that we conducted a large sample survey of chronic pain and obesity in the elderly. To the best of our knowledge, this is the first epidemiological survey on this topic among older adults in mainland China. Another strength of this survey was that we explored not only the association between total chronic pain and obesity but also the association between chronic pain locations and obesity. In addition, the associations were proven to be different. There are also limitations in this study. First, the treatment of pain was not surveyed in this study. Some participants did not report pain due to having had treatment for pain, which would lead to an underestimation of the true associations. Second, the design method of this study was a cross-sectional approach. It restricted the ability to determine the time course between chronic pain and obesity. Therefore, we cannot determine the causal relationship between the two variables. Third, this survey was limited to one municipality of Anhui Province in China; thus, caution should be taken when generalizing the results to other populations.
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5. Conclusion Our study showed that nearly 1 in 2 older adults were suffering from chronic pain in China. It is necessary to pay attention to the prevention and management of chronic pain. Obesity is independently associated with chronic pain in the elderly. This finding may provide an important clue on how to best manage chronic pain. Conflict of interest statement None. Acknowledgements This study was supported by grants from the Talent Program of the Higher Education Revitalization Plan of Anhui Province, China, 2013 17
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