Abstract
Background
Previous studies on the association between pain and cognitive decline or impairment have yielded mixed results, while studies from low- and middle-income countries (LMICs) or specifically on mild cognitive impairment (MCI) are scarce. Thus, we investigated the association between pain and MCI in LMICs and quantified the extent to which perceived stress, sleep/energy problems, and mobility limitations explain the pain/MCI relationship.
Methods
Data analysis of cross-sectional data from six LMICs from the Study on Global Ageing and Adult Health (SAGE) were performed. MCI was based on the National Institute on Aging-Alzheimer's Association criteria. "Overall in the last 30 days, how much of bodily aches or pain did you have?” was the question utilized to assess pain. Associations were examined by multivariable logistic regression analysis and meta-analysis.
Results
Data on 32,715 individuals aged 50 years and over were analysed [mean (SD) age 62.1 (15.6) years; 51.7% females]. In the overall sample, compared to no pain, mild, moderate, and severe/extreme pain were dose-dependently associated with 1.36 (95% CI = 1.18–1.55), 2.15 (95% CI = 1.77–2.62), and 3.01 (95% CI = 2.36–3.85) times higher odds for MCI, respectively. Mediation analysis showed that perceived stress, sleep/energy problems, and mobility limitations explained 10.4%, 30.6%, and 51.5% of the association between severe/extreme pain and MCI.
Conclusions
Among middle-aged to older adults from six LMICs, pain was associated with MCI dose-dependently, and sleep problems and mobility limitations were identified as potential mediators. These findings raise the possibility of pain as a modifiable risk factor for developing MCI.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Dementia is a syndrome characterized by deterioration in cognitive function beyond what might be expected from the usual consequences of biological ageing, which impacts on function [1]. Globally, approximately 55 million people are living with dementia, with over 60% of these people living in low- and middle-income countries (LMICs). Owing to the proportion of older adults increasing in nearly every country, the number of dementia cases is expected to increase to approximately 78 million in 2030, and 139 million in 2050 [1]. Such a high and rising rate of dementia is a significant problem as it is one of the major causes of morbidity and mortality in older adults, and its social and economic impacts are substantial. For example, in 2019, the estimated total global societal cost of dementia was US$ 1.3 trillion, and these costs are expected to surpass US$ 2.8 trillion by 2030 as both the number of people living with dementia and care costs increase [1].
However, to date, there is no treatment for any form of dementia which slows disease progression. Therefore, it is of utmost importance to identify risk factors that can be modified at the precursory stage of dementia to inform targeted intervention to prevent or delay the onset of dementia. Mild cognitive impairment (MCI) is a preclinical state of dementia with annual conversion rates to dementia ranging from 10 to 15% in clinical samples and 3.8% to 6.3% in community-based samples [2]. MCI is considered an important target for intervention in the prevention or delay of dementia.
One understudied but potentially important risk factor for MCI in the context of LMICs is pain. The prevalence of pain increases with age and can be particularly high in LMICs due to limited availability of proper treatment. For example, the World Health Organization (WHO) estimates that 5.5 billion people (more than 80% of the global population) do not have access to treatments for moderate to severe pain and that most of these people live in LMICs [3]. Pain may plausibly increase risk for MCI via several mechanisms. For instance, perceived stress, sleep problems, and low physical activity due to mobility limitations can all be consequences of pain, and all these factors have been reported to be risk factors for MCI [4,5,6,7,8,9,10,11]. Furthermore, pain has been found to be associated with brain plasticity and structural changes in different cortical regions associated with learning, memory, fear, and emotional responses [12]. However, previous studies on the association between pain and cognitive decline or impairment have yielded conflicting results, with some studies finding positive associations while others no associations, and this may be due to the different definitions of cognitive decline employed [13, 14]. Importantly, to date, the relationship between pain and MCI that is distinctly different from general cognitive decline (which may not necessarily be related with higher risk for future dementia onset) has not been examined. Clearly, investigations from diverse settings are required. Moreover, it is important to elucidate the potential mediators in the association between pain and MCI for targeted prevention efforts. Given this background, the aim of the present study was to investigate the association between pain and MCI in a representative sample of 32,715 individuals aged 50 years and over from six LMICs. A further aim was to identify to what extent perceived stress, sleep/energy problems, and mobility limitations may explain the association between pain and MCI.
Methods
Secondary data analysis of the Study on Global Ageing and Adult Health (SAGE) 2007–2010 was performed. The main aim of this survey was to obtain comparable and valid information on wellbeing and health among middle-aged and older adults. China, Ghana, India, Mexico, Russia, and South Africa were the countries that participated in the survey. Of note, two of the most populous countries in the world were included (i.e., China and India). Based on the World Bank classification when the survey was conducted, Ghana and India were a low-income country and a lower middle-income country, respectively, while Mexico, Russia, and South Africa were upper middle-income countries. China was a lower middle-income country at the beginning of the survey but became an upper middle-income country in 2010. Details of the survey methodology have been published elsewhere [15]. In brief, a multistage clustered sampling design method was employed to obtain samples which are nationally representative. The sample consisted of adults aged 18 years and over, while people aged 50 years and over were oversampled. Trained interviewers performed face-to-face interviews utilizing a standard questionnaire. Standard translation procedures were undertaken so that the survey is comparable between countries. Computer-assisted personal interview (CAPI) was used in half of the interviews in China, and the other half was done using paper and pencil. Mexico used only CAPI, while the other four countries used paper and pencil format for all interviews. The survey response rates ranged from 53% in Mexico to 93% in China. The population structure based on the United Nations Statistical Division was adjusted for with the use of sampling weights. The WHO Ethical Review Committee and local ethics research review boards provided ethical approval. All participants gave written informed consent.
Mild cognitive impairment (MCI)
The recommendations of the National Institute on Aging-Alzheimer's Association were followed to identify people with MCI [16]. Identical algorithms utilized in previous SAGE publications were employed [17, 18]. In brief, fulfilling all following conditions corresponded to MCI:
-
(I)
Impairment in at least one cognitive domain based on objective measures: corresponded to < -1 SD cut-off after adjustment for age, level of education, and country, on at least one of the following cognitive function tests: animal naming task [19], examining verbal fluency; word list immediate/delayed verbal recall of the Consortium to Establish a Registry for Alzheimer's Disease [19], assessing episodic and learning memory; digit span forward/backwards of the Weschler Adult Intelligence Scale [20], evaluating working and attention memory;
-
(II)
Maintenance of functional ability independence: Questions on past 30-day basic activities of daily living (ADL) were used to assess this condition [21]: “How much difficulty did you have with eating (including cutting up your food)?” and “How much difficulty did you have in getting dressed?” Answering none, mild, or moderate to both of these questions referred to this condition, and all other people (i.e., those who answered ‘severe’ or ‘extreme’) were omitted from the analysis (935 participants aged 50 years or more).
-
(III)
Worry regarding changing cognition: This was assessed by two questions: How would you best describe your memory at present?” and “Compared to 12 months ago, would you say your memory is now better, the same or worse than it was then?” Answering ‘bad’ or ‘very bad’ to the first question and/or responding ‘worse’ to the second corresponded to this condition.
-
(IV)
Absence of dementia: People who were unable to participate in the survey due to severe cognitive impairment were omitted from the current study.
Pain
“Overall in the last 30 days, how much of bodily aches or pain did you have?” was the question used to assess the level of pain with answer options ‘none’, ‘mild’, ‘moderate’, ‘severe’ and ‘extreme’. In the current analysis, ‘severe’ and ‘extreme’ were merged into one category as there were very few people who answered ‘extreme’. Furthermore, a dichotomized variable [i.e., severe/extreme pain (yes/no)] was also created and employed in some analyses.
Mediators
The potential mediators examined in the current study included perceived stress, sleep/energy problems, and mobility limitations, and these were selected since they can be the consequence of pain, while they may also potentially increase future risk of cognitive decline [22,23,24]. Two questions each were used to assess these three conditions. The actual questions are described in supplementary Table S1. Each question was based on a five-point scale with answer options ranging from 'none' to ‘extreme/cannot do’ except for the two items on perceived stress, ranging from ‘never’ to ‘very often’. Based on the two questions for each individual condition, we utilized factor analysis with polychoric correlations to calculate a factor score, subsequently converted to scores ranging from 0 to 100, with higher values corresponding to worse health status [25].
Control variables
Past literature was used as a guide to select the control variables [26], and these included age, sex, education (years), wealth quintiles based on income, marital status (married/cohabiting, never married, separated/divorced/widowed), past 30-day alcohol consumption, smoking (never, current, past), body mass index (BMI), number of chronic conditions, and depression. BMI was estimated as measured weight in kilograms divided by measured height in metres squared. BMI was categorized as underweight (BMI < 18.5 kg/m2), normal weight (BMI 18.5–24.9 kg/m2), overweight (BMI 25.0–29.9 kg/m2), and obesity (BMI ≥ 30 kg/m2) based on WHO guidelines [27]. Information on 10 chronic physical diseases (angina, stroke, arthritis, asthma, hearing problem, diabetes, edentulism, hypertension, visual impairment, chronic lung disease) were gathered. Table S2 (Appendix) provides complete details on how the diagnosis was determined. The number of chronic conditions was calculated per participant and categorized as 0, 1, and ≥ 2. Questions based on the World Mental Health Survey version of the Composite International Diagnostic Interview [28] were used for the endorsement of past 12-month DSM-IV depression.
Statistical analysis
The statistical analysis was done with Stata 14.2 (Stata Corp LP, College station, Texas). The analysis was limited to people aged 50 years and over. The difference in sample characteristics was tested by Student’s t-tests and Chi-squared tests for continuous and categorical variables, respectively. Multivariable logistic regression analysis was performed to examine the association between severity of pain (four-category variable with values ‘none’, ‘mild’, ‘moderate’, ‘severe/extreme’) (exposure) and MCI (outcome). We also conducted test of trend to assess whether increasing severity of pain was dose-dependently associated with higher odds for MCI by including the variable on severity of pain as a continuous variable rather than a categorical variable in the model. The analysis was also stratified by age group (50–64 and 65 years and over) and sex. Next, to assess the degree of between-country heterogeneity in the association between severe/extreme pain and MCI, we conducted country-wise analysis and calculated the Higgin’s I2 which represents the level of heterogeneity that is not due to sampling error with a value of < 40% frequently viewed as negligible and 40–60% as moderate heterogeneity [29]. An overall estimate was calculated by meta-analysis with fixed effects based on country-wise estimates.
Finally, to quantify the degree to which perceived stress, sleep/energy problems, and mobility limitations may explain the association between extreme/severe pain and MCI, we conducted mediation analysis using the khb (Karlson Holm Breen) command in Stata [30]. This method can be used in logistic regression models and decomposes the total effect (i.e., unadjusted for the mediator) of a variable into direct (i.e., the effect of extreme/severe pain on MCI adjusted for the mediator) and indirect effects (i.e., the mediational effect). Using this method, the percentage of the main association explained by the mediator can also be calculated (mediated percentage). Each potential mediator was included in the model separately.
All regression analyses including the mediation analysis were adjusted for age, sex, education, wealth, marital status, alcohol consumption, smoking, BMI, chronic physical conditions, depression, and country, except for the sex- and country-stratified analyses which were not adjusted for sex and country, respectively. Adjustment for country was done by including dummy variables for each country in the model as in previous SAGE publications [17, 31]. The sample weighting and the complex study design were considered in all analyses. Results from the regression analyses are presented as odds ratios (ORs) with 95% confidence intervals (CIs). The level of statistical significance was set at P < 0.05.
Results
The final sample included 32,715 individuals (China n = 12,815; Ghana n = 4201; India n = 6191; Mexico n = 2070; Russia n = 3766; South Africa n = 3672) aged 50 years and over with preservation in functional abilities. The prevalence of MCI was 15.3%, while the prevalence of different severity of pain were: mild 32.7%; moderate 18.1%; severe/extreme 9.6%. The sample characteristics are shown in Table 1. The mean (SD) age was 62.1 (15.6) years and 51.7% were females. People with severe/extreme pain had much worse scores in terms of perceived stress, sleep/energy problems, and mobility limitations, compared to those without this condition. The prevalence of MCI increased linearly with increasing severity of pain (Fig. 1). For example, the prevalence of MCI was only 11.9% among those with no pain but this increased to 20.9% among those with severe/extreme pain. After adjustment for potential confounders, in the overall sample, compared to no pain, mild, moderate, and severe/extreme pain were dose-dependently associated with 1.36 (95% CI = 1.18–1.55), 2.15 (95% CI = 1.77–2.62), and 3.01 (95% CI = 2.36–3.85) times higher odds for MCI (Table 2). Age-stratified analysis showed that this association is slightly more pronounced among those aged 50–64 years, while sex-stratified analysis showed that the association is similar among males and females. Country-wise analysis showed that severe/extreme pain is significantly associated with higher odds for MCI in all countries with a negligible level of between-country heterogeneity (I2 = 35.9%) (Fig. 2). Finally, mediation analysis showed that perceived stress, sleep/energy problems, and mobility limitations explained 10.4%, 30.6%, and 51.5% of the association between severe/extreme pain and MCI (Table 3).
Discussion
Main findings
Increasing severity of pain was dose-dependently associated with higher odds for MCI. For example, in the overall sample, compared to no pain, severe/extreme pain was significantly associated with 3.01 times higher odds for MCI. Severe/extreme pain was significantly associated with higher odds for MCI in all countries included in the study, with between-country heterogeneity being of a negligible level. Mobility limitations explained more than half of the association between pain and MCI, followed by sleep/energy problems (30.6%), and perceived stress (10.4%). To the best of our knowledge, this is the first study on the association between pain and MCI in LMICs as well as the first to identify its potential mediators.
Interpretation of the findings
There are several mechanisms that may explain the link between pain and MCI. First, mobility limitations explained more than half of the association between pain and MCI. Pain may lead to mobility limitations via, for example, avoidance of activities of daily living and physical activity that may exacerbate pain [7]. Thus, mobility limitations may lead to low levels of physical activity, and this is a known risk factor for cognitive decline [32]. Apart from this, mobility limitations (e.g., gait dysfunction) per se may increase risk for MCI via the presence of neurofibrillary tangles in the substantia nigra as well as leading to decreased cognitive stimulation more generally [8]. Second, sleep/energy problems explained more than 30% of the association between pain and MCI. Pain may induce sleep problems via an increase in depressive symptoms [33] as well as difficulty falling asleep and movement during sleep [5]. In turn, sleep problems may lead to higher risk of MCI through the accumulation and impaired clearance of toxic metabolites in the brain [6] or inflammation [34, 35]. Next, perceived stress was also identified as a potential mediator but to a lesser extent. Pain can lead to perceived stress not only via pain itself but also through its impact on quality of life [4]. Moreover, pain and stress share significant conceptual and physiological overlaps both challenging the body’s homeostasis [4]. Perceived stress has been hypothesized to increase risk for MCI through mechanisms such as dysregulation of hormones (e.g., cortisol) and increased production of pro-inflammatory cytokines, which can impair the neural structure and function implicated in cognitive performance [36]. Finally, apart from these pathways, pain may affect brain plasticity and cause structural changes in different cortical regions associated with cognition [12].
Implications of the study findings
The present study suggests that pain may be a modifiable risk factor for cognitive impairment among middle-aged and older adults in LMICs, and that addressing pain may prevent cognitive impairment in this context. Directly mitigating pain may potentially have a large impact especially in the context of LMICs as more than 80% of the global population lacks access to treatments for moderate to severe pain, and the majority of these individuals live in LMICs [3]. Indeed, organizations such as the International Association for the Study of Pain and the World Federation of Societies of Anesthesiologists are working with national professional societies to develop local expertise and leadership for pain management. Furthermore, it will be important for research to expand also in services including non-pharmacological based therapies such as music therapy [37], especially for areas where access to health facilities or analgesics is limited. Finally, considering the role of mobility, workers delivering pain treatments should be able to promote and to support people to address barriers to physical activity.
Strengths and limitations
The use of a large nationally representative dataset from six LMICs and the identification of potential mediators in the pain/MCI relationship are clear strengths of the present study. However, findings must be interpreted in light of their limitations. First, the study is cross-sectional in nature, and thus, causality or temporal associations cannot be determined. In relation to this, it is possible for the mediational effect calculated in our study to be an overestimation due to the various pathways that may exist between pain, MCI, and the potential mediators. Second, we had no information on analgesic drugs. Opioids or anticholinergic antidepressants such as amitriptyline may themselves lead to cognitive decline [38], and thus, we were unable to know the extent to which the association between pain and cognitive decline may be explained by use of analgesics. Finally, our study was not designed to clinically diagnose dementia. Therefore, while it is possible for some people with mild dementia to have been included in our sample, the prevalence of MCI in our study was within previously reported figures [39].
Conclusion
In this large representative sample of middle-aged to older adults from six LMICs, pain was associated with MCI dose-dependently. Moreover, mobility limitations and sleep/energy problems accounted for a large proportion of the association. Addressing pain among middle-aged and older adults may aid in the prevention of MCI and ultimately dementia in the context of LMICs, but future longitudinal and intervention studies would be necessary to confirm this.
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
World Health Organization (2021) Dementia. https://www.who.int/news-room/fact-sheets/detail/dementia
Bohlken J, Jacob L, Kostev K (2019) Progression of mild cognitive impairment to dementia in German specialist practices. Dementia 18:380–390
Seya M-J, Gelders SFAM, Achara OU et al (2011) A first comparison between the consumption of and the need for opioid analgesics at country, regional, and global levels. J Pain Palliat Care Pharmacother 25:6–18
Abdallah CG, Geha P (2017) Chronic pain and chronic stress: two sides of the same coin? Chronic Stress 1:2470547017704763
Kelly GA, Blake C, Power CK et al (2011) The association between chronic low back pain and sleep: a systematic review. Clin J Pain 27:169–181
Lucey BP, Hicks TJ, McLeland JS et al (2018) Effect of sleep on overnight cerebrospinal fluid amyloid β kinetics. Ann Neurol 83:197–204
Dueñas M, Ojeda B, Salazar A et al (2016) A review of chronic pain impact on patients, their social environment and the health care system. J Pain Res 9:457–467
Buracchio T, Dodge HH, Howieson D et al (2010) The trajectory of gait speed preceding mild cognitive impairment. Arch Neurol 67:980–986
Kushkestani M, Parvani M, Ghafari M et al (2022) The role of exercise and physical activity on aging-related diseases and geriatric syndromes. Sport TK-Rev Euroam Cienc Del Deport 11:6
Hoti F (2021) Impact of physical activity on longevity: A review of the literature. Atena J Public Heal 3:4
Esparza Montero MÁ (2021) Influence of the strength of the ankle plantar flexors on dynamic balance in 55–65-year-old women. Atena J Public Heal 3:3
Mazza S, Frot M, Rey AE (2018) A comprehensive literature review of chronic pain and memory. Prog Neuro-Psychopharmacol Biol Psychiatry 87:183–192
Zhang X, Gao R, Zhang C et al (2021) Evidence for cognitive decline in chronic pain: a systematic review and meta-analysis. Front Neurosci 15:737874
Innes KE, Sambamoorthi U (2020) The potential contribution of chronic pain and common chronic pain conditions to subsequent cognitive decline, new onset cognitive impairment, and incident dementia: A systematic review and conceptual model for future research. J Alzheimer’s Dis 78:1177–1195
Kowal P, Chatterji S, Naidoo N et al (2012) Data resource profile: the world health organization study on global AGEing and adult health (SAGE). Int J Epidemiol 41:1639–1649. https://doi.org/10.1093/ije/dys210
Albert MS, DeKosky ST, Dickson D et al (2011) The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement 7:270–279
Koyanagi A, Lara E, Stubbs B et al (2018) Chronic physical conditions, multimorbidity, and mild cognitive impairment in low-and middle-income countries. J Am Geriatr Soc 66:721–727
Koyanagi A, Oh H, Vancampfort D et al (2019) Perceived stress and mild cognitive impairment among 32,715 community-dwelling older adults across six low-and middle-income countries. Gerontology 65:155–163
Moms JC, Heyman A, Mohs RC et al (1989) The consortium to establish a registry for alzheimer’s disease (CERAD) part I clinical and neuropsychological assesment of alzheimer’s disease. Neurology 39:1159
Corporation P (2002) WAIS‐III WMS‐III: Updated technical manual
Katz S, Ford AB, Moskowitz RW et al (1963) Studies of illness in the aged: the index of ADL: a standardized measure of biological and psychosocial function. JAMA 185:914–919
Katz MJ, Derby CA, Wang C et al (2016) Influence of perceived stress on incident amnestic mild cognitive impairment: results from the einstein aging study. Alzheimer Dis Assoc Disord 30:93
de Almondes KM, Costa MV, Malloy-Diniz LF et al (2016) Insomnia and risk of dementia in older adults: systematic review and meta-analysis. J Psychiatr Res 77:109–115
Guure CB, Ibrahim NA, Adam MB et al (2017) Impact of physical activity on cognitive decline, dementia, and its subtypes: meta-analysis of prospective studies. Biomed Res Int 2017:1–13
Stubbs B, Vancampfort D, Firth J et al (2018) Relationship between sedentary behavior and depression: a mediation analysis of influential factors across the lifespan among 42,469 people in low-and middle-income countries. J Affect Disord 229:231–238. https://doi.org/10.1016/j.jad.2017.12.104
Veronese N, Koyanagi A, Solmi M et al (2018) Pain is not associated with cognitive decline in older adults: a four-year longitudinal study. Maturitas 115:92–96
World Health Organization (2021) Obesity and overweight. https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight. Accessed 14 Feb 2022
Kessler RC, Üstün TB (2004) The world mental health (WMH) survey initiative version of the world health organization (WHO) composite international diagnostic interview (CIDI). Int J Methods Psychiatr Res 13:93–121
Higgins JPT, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21:1539–1558
Breen R, Karlson KB, Holm A (2013) Total, direct, and indirect effects in logit and probit models. Sociol Methods Res 42:164–191
Koyanagi A, Garin N, Olaya B et al (2014) Chronic conditions and sleep problems among adults aged 50 years or over in nine countries: a multi-country study. PLoS ONE 9:e114742
Yaffe K, Barnes D, Nevitt M et al (2001) A prospective study of physical activity and cognitive decline in elderly women: women who walk. Arch Intern Med 161:1703–1708
Sayar K, Arikan M, Yontem T (2002) Sleep quality in chronic pain patients. Can J Psychiatry 47:844–848
Robbins R, Weaver MD, Barger LK et al (2021) Sleep difficulties, incident dementia and all-cause mortality among older adults across 8 years: findings from the national health and aging trends study. J Sleep Res 30:e13395
Smith L, Il SJ, Jacob L et al (2021) Sleep problems and mild cognitive impairment among adults aged≥ 50 years from low-and middle-income countries. Exp Gerontol 154:111513
Greenberg MS, Tanev K, Marin M et al (2014) Stress, PTSD, and dementia. Alzheimer’s Dement 10:S155–S165
Odell-Miller H (2021) Embedding music and music therapy in care pathways for people with dementia in the 21st century—a position paper. Music Sci 4:20592043211020424
Pickering G, Pereira B, Clère F et al (2014) Cognitive function in older patients with postherpetic neuralgia. Pain Pract 14:E1–E7
Petersen RC (2016) Mild cognitive impairment. Contin Lifelong Learn Neurol 22:404
Acknowledgements
This paper uses data from WHO’s Study on Global Ageing and Adult Health (SAGE). SAGE is supported by the U.S. National Institute on Aging through Interagency Agreements OGHA 04034785, YA1323–08-CN-0020, Y1-AG-1005–01 and through research grants R01-AG034479 and R21-AG034263. BRUs post is part funded by a philanthropic donation from Gnodde Goldman Sachs giving.
Funding
Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Dr. Guillermo F. López Sánchez is funded by the European Union—Next Generation EU.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
None.
Ethical approval
The WHO Ethical Review Committee and local ethics research review boards provided ethical approval.
Statement of human and animal rights
No animals were involved in this study.
Informed consent
All participants gave written informed consent.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
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/.
About this article
Cite this article
Smith, L., López Sánchez, G.F., Shin, J.I. et al. Pain and mild cognitive impairment among adults aged 50 years and above residing in low- and middle-income countries. Aging Clin Exp Res 35, 1513–1520 (2023). https://doi.org/10.1007/s40520-023-02434-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40520-023-02434-7