A Cross-Sectional and Longitudinal Study of Pain among Middle-Aged and Older Adults in Thailand

Pengpid, Supa; Peltzer, Karl

doi:https://doi.org/10.1155/2023/1158899

Pain Research and Management

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Abstract Introduction Methods Results Discussion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 1158899 | https://doi.org/10.1155/2023/1158899

A Cross-Sectional and Longitudinal Study of Pain among Middle-Aged and Older Adults in Thailand

Supa Pengpid^1,2,3and Karl Peltzer^1,4,5

Academic Editor: Vahid Rakhshan

Received12 Aug 2022

Revised13 Dec 2022

Accepted03 Mar 2023

Published09 Mar 2023

Abstract

Objective. The present study aimed to assess the prevalence and risk factors of pain among ageing adults in Thailand. Methods. Cross-sectional and longitudinal data were analysed from two consecutive national waves of the Health, Aging, and Retirement in Thailand (HART) study in 2015 and 2017. The dependent variable pain was defined as moderate or severe pain in any of the 13 areas of the body over the past month. Independent variables included sociodemographic factors, health risk behaviour, physical and mental health conditions, and healthcare utilization. Results. The baseline or cross-sectional sample consisted of 5,616 participants (≥45 years), and the follow-up or incident sample consisted of 2,305 participants. The proportion of pain in the cross-sectional/baseline sample was 36.0%, and in the incident/follow-up sample 39.9%. In the cross-sectional/baseline multivariable model, poor self-reported mental health, sleep problem, arthritis or rheumatism, brain disease and/or psychiatric problems, lung disease, use of hospital in-patient, conventional out-patient, and traditional medicine practitioners were positively associated with pain. In the incident/follow-up multivariable model, older age, Buddhist religion, class I obesity, poor self-reported mental health, hospital in-patient, private clinic out-patient, and use of a practitioner of traditional medicine were positively associated with pain. Male sex and higher education were negatively associated with both cross-sectional and incident pain. Conclusions. More than one-third of older adults in Thailand had past month moderate or severe pain. Risk factors of pain from cross-sectional and/or incident analysis included older age, female sex, lower education, obesity, poor self-reported mental health, sleep problem, arthritis or rheumatism, brain disease and/or psychiatric problems, lung disease, and conventional and traditional healthcare utilization.

1. Introduction

The population in Thailand is rapidly ageing, increasing the health burden of older adults [1]. In the general population, pain is a common symptom, comorbid with clinical conditions, and the prevalence ranges from 10% to 60% [2]. Pain has been redefined by the International Association for the Study of Pain as “An unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage [3].”

Data from adults in the US even have shown an increase in noncancer pain from 32.9% in 1997/1998 to 41.0% in 2013/2014 [4]. In middle-income countries, past 12-month mild, moderate, or severe pain was reported by 14.3% in Brazil, 6.2% in China, and 18.3% in Russia [5]. In the 2008 National Health and Wellness Survey in the UK, France, Italy, Germany, and Spain, the prevalence of moderate or severe pain in the adult population in the past month was 16.6% [6]. In a multicountry study among older adults, 39.5% developed pain between baseline and 5 years’ follow-up [2]. In a national study that included people aged 60 and older in China, 32.5% reported having pain [7]. We lack incident and cross-sectional data on pain in general populations, such as in Southeast Asia and Thailand [2], which led to this study.

As reviewed by Raggi et al. [2], risk factors for pain may include sociodemographic factors such as lower education, female sex, older age, and mental health problems, including sleep problems, depressive symptoms, chronic conditions, such as stroke, diabetes, and obesity, and health risk behaviours, such as physical inactivity and smoking. In the multicountry study, cross-sectional risk factors for pain included obesity, female sex, and vigorous physical activity, and incident risk factors included walking difficulties, poor self-rated health, and sleep problems [2].

In addition, pain has been found to increase healthcare utilization. In a study among older adults in China, the pain was associated with an increased use of herbal medicine and over-the-counter drugs [7], in a study among adults in France, the pain was associated with more visits from healthcare providers, including emergency room visits and hospitalizations [8], and among adults with pain in a multicountry study (Brazil, China, Russia, Japan, USA, and developed European Union), physician visits, hospitalizations, and emergency room visits increased [5]. Considering the impact of pain on the life of older people, it is important to understand the pattern of pain characteristics and its risk factors, as well as healthcare utilization in Southeast Asia, including Thailand. The results may have implications for improving healthcare for patients with pain. Therefore, the objective of this cross-sectional and longitudinal study was to assess the prevalence and risk factors of pain in a national population-based sample of ageing adults in Thailand.

2. Methods

2.1. Study Design, Setting, and Participants

Cross-sectional and longitudinal national data were analysed from two consecutive surveys of the Health, Aging, and Retirement in Thailand (HART) study in 2015 and 2017 [9]. In a national sample, one household member (≥45 years) (inclusion criteria) was randomly selected by applying a multistage sampling process [9]; detailed procedures have been published [10]. The sample size of the 2015 survey was 5,616, and the sample of the 2017 survey was 3,708 participants, 1,908 were lost to follow-up, and the response and retention rate was 72.3% and 66.03%, respectively [11]. At baseline, a paper and pencil (PAPI) questionnaire were used, and at follow-up computer-assisted personal interviewing (CAPI) [9]. The Ethics Committee on Human Research of the National Institute of Development Administration, ECNIDA (ECNIDA 2020/00012), approved the study, and participants gave written informed consent [11]. We use Epi Info Version 7.2.2.6 for the calculation of the population survey sample size, prevalence of the previous study 32.5% pain [7], acceptable margin of error = 5, designing effect 1, at confidence level 99.99%, and the minimum sample required is 1327.

2.2. Measures

Outcome Variable Pain

HART examines past-month pain in 13 body parts (“the head, shoulders, arms, wrists, fingers, chest, abdomen, back, hips, legs, knees, ankles, and toes”) by asking the following question, “Did you feel any pain or ache in the following body parts in the last month?” Response options were none, mild, moderate, or severe. We defined moderate or severe pain in the past 4 weeks in one or more of the 13 body parts as “pain.” Cronbach alpha for the pain measure was 0.81 and 0.82 in wave 1 and wave 2, respectively.

Healthcare utilisation was assessed with utilization of the following public and private healthcare types: (1) public healthcare: hospital admission, hospital out-patient in the past two years (district hospitals provide primary healthcare (PHC), and secondary care and regional/general hospitals provide tertiary and other specialized care), a health center in the past two years (provides PHC services), medical home visit in the past two years, and medical check-up in the past year, (2) private healthcare: private clinic in the past two years (mostly curative services), and (3) traditional medicine doctor in the past two years (is mostly private but can also be public health service) (Yes/No) [10, 12].

Sociodemographic variables included income quartile, education, sex, age, and religion [13].

Tobacco smoking, “Have you ever smoked cigarettes?” (responses were “1 = yes, and still smoke now, 2 = yes, but quit smoking, and 3 = never”).

Alcohol use, “Have you ever drunk alcoholic beverages such as liquor, beer, or wine?” (response options: “1 = yes, and still drinking now, 2 = yes, but do not drink now, and 3 = never”).

Physical activity was defined as “none = inactivity, 1–149 min/week = low activity, and ≥150 min/week = high activity” [14].

Body mass index (BMI) was classified into “underweight (<18.5 kg/m²), normal weight (18.5–22.9 kg/m²), overweight (23–24.9 kg/m²), class I obesity (25–29.9 kg/m²), and class II obesity (30 kg/m²)” based on self-reported weight and height [15].

Probable depression (≥10 scores) was measured using the Center for Epidemiologic Studies Depression (CES-D-10) scale [16]; Cronbach alpha 0.78.

Self-reported mental health status: “In general, how would you rate your mental health status?” Responses were rated from “0 = very poor to 100 excellent,” and poor mental health was defined as “0 to less than 80 and good mental health as 80–100” [11].

Sleep problem was defined as “almost always or often (versus sometimes or very rarely or never) having trouble falling asleep/insomnia in the past week” [9].

Chronic diseases (diagnosed by a healthcare provider) included diabetes, hypertension, arthritis or rheumatism, emphysema, emotional/nervous or psychiatric illness, lung diseases, Alzheimer’s disease, cardiovascular diseases, brain diseases, kidney diseases, heart disease, and heart failure [9].

Functional disability was classified as being unable to do any of the four activities of daily living (ADL) (eating, bathing, dressing, and washing) [17] (Cronbach’s α 0.94).

2.3. Statistical Analysis

Descriptive statistics were used to describe cross-sectional and incident pain by demographic and health status factors. To test for differences in proportions, Pearson chi-square tests were applied. The first Poisson regression model estimated prevalence ratios (PRs) and confidence intervals (CIs) for cross-sectional pain prevalence, and the second model compared the baseline sample without pain with incident pain (developed pain at follow-up). Variables significant at in bivariate analyses were subsequently incorporated into the multivariable models. was accepted as statistically significant. Statistical analyses were carried out using StataSE 15.0 (College Station, TX, USA); only complete cases were included in the analyses.

3. Results

3.1. Participants

The baseline or cross-sectional sample consisted of 5,616 individuals (45 years and older, 66 years median age), and the follow-up or incident sample consisted of 2,305 participants. The prevalence of pain in the cross-sectional/baseline sample was 36.0%, and the prevalence of incident pain (those who had pain at follow-up without pain at baseline) was 39.9%. In the baseline sample, most frequently moderate or severe pain was reported in the legs (19.0%), knees (18.6%), and back (11.8%), followed by hips (8.6%), arms (7.2%), shoulders (6.5%), head (5.5%), ankles (4.0%), wrists (3.1%), abdomen (3.0%), toes (2.1%), chest (1.9%), and fingers (1.8%). In the cross-sectional sample, binary analysis showed that sex, age, education, income, probable depression, body mass index, alcohol use status, self-reported mental health status, arthritis or rheumatism, sleep problem, kidney disease, hypertension, cardiovascular disease, diabetes, lung disease, functional disability, and brain disease or psychiatric problems differed significantly between persons with pain and without pain. In the incident population, binary analysis showed that sex, age, education, religion, income, smoking, physical activity, self-reported mental health status, hypertension, and diabetes differed significantly between persons with and without pain (see Table 1).

Table 2 shows the utilization of healthcare by cross-sectional and incident pain. Binary analysis in the cross-sectional population showed that all four types of healthcare utilization differed significantly between people with pain and without pain, and the binary analysis in the incident sample found that hospitalization, hospital out-patient, private clinic out-patient, and use of traditional medicine practitioner differed significantly between people with overall pain and without overall pain, as well as a leg, back, and knee pain (see Table 2).

3.2. Associations with Pain in Cross-Sectional Analysis

In the multivariable model, poor self-reported mental health (aPR: 1.49, 95% CI: 1.29 to 1.72), sleep problem (aPR: 1.99, 95% CI: 1.67 to 2.37), arthritis or rheumatism (aPR: 3.29, 95% CI: 2.36 to 4.60), lung disease (aPR: 1.94, 95% CI: 1.03 to 3.63), brain disease and/or psychiatric problems (aPR: 2.11, 95% CI: 1.12 to 3.96), hospital in-patient (aPR: 1.88, 95% CI: 1.55 to 2.28), hospital out-patient (aPR: 1.27, 95% CI: 1.10 to 1.47), private clinic out-patient (aPR: 1.51, 95% CI: 1.20 to 1.90), and use of traditional medicine practitioner (aPR: 2.41, 95% CI: 1.60 to 3.63) were positively associated, and male sex (aPR: 0.77, 95% CI: 0.67 to 0.88) and higher education (aPR: 0.63, 95% CI: 0.53 to 0.76) were inversely associated with cross-sectional pain (see Table 3).

3.3. Associations with Incident Pain

In the final multivariable model, older age (aPR: 1.02, 95% CI: 1.01 to 1.03), Buddhist religion (aPR: 1.85, 95% CI: 1.26 to 2.73), obesity class I (aPR: 1.28, 95% CI: 1.01 to 1.62), poor self-reported mental health (aPR: 1.27, 95% CI: 1.02 to 1.58), hospital in-patient (aPR: 1.75, 95% CI: 1.31 to 2.35), private clinic out-patient (aPR: 1.59, 95% CI: 1.22 to 2.07), and use of traditional medicine practitioner (aPR: 2.57, 95% CI: 1.61 to 4.09) were positively associated, and male sex (aPR: 0.80, 95% CI: 0.66 to 0.99) and higher education (aPR: 0.65, 95% CI: 0.51 to 0.83) were inversely associated with incident pain (see Table 4).

4. Discussion

It appears that this is the first study that assessed the prevalence and risk factors of pain among ageing adults (≥45 years) in a national household survey in Thailand in 2015 and 2017. The prevalence of moderate or severe pain in the cross-sectional population was 36.0% and in the incident population 39.9%. This result is similar to the prevalence of pain among older adults in China (32.5%) [7], among adults in the USA (32.9%–41.0%) [4], and in a multicountry study among older adults (39.5% incident pain) [2], but higher than among adults in Brazil, China, and Russia (ranging from 6.2% to 18.3% past 12-month mild, moderate, or severe pain, versus 70% mild, moderate, or severe past month pain in our study, analysis not shown) [5], and among adults in the UK, France, Italy, Germany, and Spain (16.6% moderate or severe past month pain) [6]. Some of these differences may be explained by social and cultural differences, as well as differences in the measurement or definition of pain.

Risk factors of pain from cross-sectional and/or incident analysis included older age, female sex, lower education, Buddhist religion, obesity class I, poor self-reported mental health, sleep problem, arthritis or rheumatism, brain disease and/or psychiatric problems, lung disease, hospital in-patient, conventional out-patient, and traditional medicine practitioner use. Our results are in line with former research in terms of older age [18], females [2, 5, 7, 18], and lower education [19, 20]. It is possible that older adults develop more physical conditions, which in turn may cause more pain in the body [18]. Compared to women, men may underreport pain due to internalized masculinity norms [18, 21]. Older adults with lower education and, in univariable analysis, lower income had a higher prevalence of pain. General education and access to financial resources can improve general health and reduce pain through health-behavioural, medical, and social factors [20]. Compared to Muslims or others, Buddhists had a higher prevalence of pain. Similarly, in a study in Thailand [22], the proportion of knee pain was lower in Muslims than in Buddhists, which may relate to differences in religious practices (“Muslims pray since childhood by forcing the knees into deep flexion, stretching the soft tissue surrounding the knee, and decrease stiffness and contact pressure of the articular cartilage”).

Consistent with previous research [2], we found that modifiable risk factors, such as obesity, poor self-reported mental health status, and sleep problems, increased the odds of pain. Obesity can contribute to pain through mechanical and inflammatory processes [2, 23]. Weight loss and sleep behaviour modification and mental health promotion can be used to address these modifiable risk factors [2]. In addition, physical exercise was found to be protective against pain in the univariable model. Although some studies show a bidirectional association between physical activity and knee pain [24]. On the other hand, some other studies found a bidirectional association between pain and sleep problems [25] and poor mental health (depressive symptoms) [26].

Furthermore, in agreement with previous research [2, 5], having certain comorbidities, such as arthritis [7] and lung diseases, increased the odds of pain. Older adults with arthritis are prone to pain disability [27], and pain is a common problem among patients with interstitial lung disease [28]. In addition, in univariable analyses, other noncommunicable diseases (NCDs), such as hypertension and diabetes were associated with pain. Given the large burden of NCDs in Thailand and the region, it may be important to synergistically target NCDs and pain relief interventions [7]. Moreover, in line with previous findings [5, 7, 8], pain increased hospital admission, conventional, and traditional medicine out-patient care visits. More research is needed to explore the specific pain management strategies, for example for the most frequently occurring pains related to the legs, knees, and back, by the different types of healthcare providers in Thailand. For example, a “Thai Medicinal Plant-4 (TMP-4) cream made up of Garcinia mangostana peel, Sesamum indicum seeds, Glycine max (L.) Merr. Seeds, and Centella asiatica leaves were not inferior to diclofenac gel in relieving osteoarthritic knee pain.” [29].

Study limitations include the evaluation of the self-report of all study variables. Furthermore, chronic pain and care or treatment modalities for pain were not assessed. Furthermore, the survey excluded institutionalised persons.

In conclusion, more than one-third of older adults in Thailand had past month moderate or severe pain. Risk factors of pain from cross-sectional and/or incident analysis included older age, female sex, lower education, Buddhist religion, obesity class I, poor self-reported mental health, sleep problem, arthritis or rheumatism, brain disease and/or psychiatric problems, lung disease, hospital in-patient, conventional out-patient, and traditional medicine practitioner use. The findings of this study may guide healthcare professionals and clinicians in improving pain management strategies. Additional investigations are indicated to assess chronic pain and types of pain management strategies by older adults in Thailand.

Data Availability

The data source is publicly available at Gateway to Global Ageing Data, Health, Aging, and Retirement in Thailand: https://g2aging.org/?section=study&studyid=44.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The Health, Aging, and Retirement in Thailand (HART) study was sponsored by the Thailand Science Research and Innovation (TSRI) and National Research Council of Thailand (NRCT).

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Copyright

Copyright © 2023 Supa Pengpid and Karl Peltzer. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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