Volume 21, Issue 2 (6-2024)
J Res Dev Nurs Midw 2024, 21(2): 40-42 |
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Rostami Z, Rahmati M, Rostamnia L, salari N. Mild cognitive impairment and its determinants in urban and rural areas among older adults:
A Cross-Sectional Study. J Res Dev Nurs Midw 2024; 21 (2) :40-42
URL: http://nmj.goums.ac.ir/article-1-1677-en.html
URL: http://nmj.goums.ac.ir/article-1-1677-en.html
1- International Student Research Committee, Kermanshah University of Medical Sciences (KUMS), Kermanshah, Iran
2- Department of Geriatric and Psychiatric Nursing, School of Nursing and Midwifery, Kermanshah University of Medical Sciences (KUMS), Kermanshah, Iran
3- Department of Nursing, School of Nursing and Midwifery, Kermanshah University of Medical Sciences, Kermanshah, Iran ,l.rostamniya@gmail.com
4- Department of Biostatistics, Faculty of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
2- Department of Geriatric and Psychiatric Nursing, School of Nursing and Midwifery, Kermanshah University of Medical Sciences (KUMS), Kermanshah, Iran
3- Department of Nursing, School of Nursing and Midwifery, Kermanshah University of Medical Sciences, Kermanshah, Iran ,
4- Department of Biostatistics, Faculty of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
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Introduction
Due to medical advancements that have increased life expectancy, the proportion of people over 60 is expected to double by 2050, especially in developing countries like Iran (1,2).
Mild Cognitive Impairment (MCI) signifies a critical juncture between the cognitive changes associated with normal aging and the onset of more severe cognitive decline, such as dementia (3). In older adults, experiencing difficulties with information processing and memory retention can significantly impact their ability to carry out daily tasks, ultimately eroding their independence (4).
Cognitive function is influenced by various factors in urban and rural settings (5,6). Urban areas face challenges like pollution, noise, social isolation, and healthcare access that impact cognitive function (7). Conversely, rural areas contend with limited healthcare access, lower education levels, labor-driven activities, and tighter social networks (5).
This study aimed to investigate the MCI status in rural and urban areas while exploring the relationships between factors such as stress management, social and interpersonal relationships, socioeconomic status, and MCI in older adults residing in these diverse environments.
Methods
A cross-sectional study with a descriptive-analytic approach was conducted to meet the study objectives. The study included individuals aged 60 years and older with health records in Kermanshah City's healthcare centers. Inclusion criteria comprised age 60 or older, informed consent, no history of psychotic or cognitive disorders, and no hearing or speech impairments. Exclusion criteria included unwillingness to cooperate or incomplete examination/questionnaire.
The minimum required sample size was calculated to be 460 people based on Bae et al.'s study and the variance of the Mini-Mental State Examination (MMSE) score (8). A 10% sample loss was added to the target volume to ensure more accurate results. Consequently, the target sample size was 506 people.
Multi-stage cluster sampling was used, initially stratifying the sample into urban and rural areas. Kermanshah City was divided into eight clusters based on municipal divisions, including 24 urban areas and 77 health centers. Rural areas were divided into 12 comprehensive rural health service centers and 149 rural health centers, serving as clusters. Cluster selection was based on the population distribution of individuals aged 60 and older, with 17% in rural areas and 83% in urban areas. Twenty clusters were identified, with four from rural and 16 from urban health centers. Samples were randomly chosen within each center after obtaining written consent.
Data Collection
After obtaining the necessary approvals and permits in 2022, the researcher visited health centers in rural and urban Kermanshah armed with an introduction letter. Samples were randomly selected within each center. Eligible individuals were contacted by phone, briefed on the study, and invited to participate. After providing informed consent, the participants completed questionnaires via interviews. Data collection took place from March to September 2023, spanning six months.
Measures
Cognitive function was assessed using MMSE, and the education-specific cutoff points of total MMSE scores were used to identify MCI. The cutoff points for total MMSE scores were ≤ 19 for illiterate individuals, ≤ 22 for participants with high school education, and ≤ 26 for those with a diploma or higher educational level (9). The reliability and validity of the MMSE questionnaire in Iran were established by Seydian et al., who reported a satisfactory Cronbach's alpha coefficient of 0.81 for internal reliability (10).
Stress management and social relationships were evaluated using a scale developed by Ishaghi et al. The scale, originally comprising 46 questions across five domains, was adapted for this study. It assessed stress management with five questions and social relationships with seven questions. Responses ranged from 'very low' to 'very high' on a 5-point Likert scale. Internal correlational coefficients were 0.71 for stress management and 0.81 for social relationships, yielding a total Cronbach's alpha coefficient of 0.76 (11).
Data analyses
Data were analyzed using SPSS version 16, employing the Logistic Regression test, Kruskal-Wallis test, and Mann-Whitney U test due to the non-normal distribution of the data. Univariate logistic regression analyses were conducted to examine the relationship between each independent variable and MCI. Variables were included in the analysis if they had a p-value of less than 0.01 to ensure a high level of statistical rigor. The variables included in the analysis were social relationships, stress management, age, and gender. The estimated indices for the logistic regression analysis included odds ratios and 95% confidence intervals. Statistical significance was set at p < 0.05 for all analyses.
Results
A total of 506 older adults participated, 419 from urban areas and 87 from rural areas in Kermanshah. Among them, 54% were men, and 70% were illiterate, with an 80% illiteracy rate among rural participants. Forty-five percent of the participants were aged between 60 and 65 years old. Details are summarized in Table 1. The overall prevalence of MCI was 21.9%. Urban areas showed a 15% prevalence, while rural areas had a higher prevalence rate of 55%.
Significant differences were found in social relationships between the MCI and healthy cognitive groups in both urban (p = 0.001) and rural (p = 0.04) areas, according to the Mann-Whitney U test. However, stress management significantly differed between the groups only in urban areas (p = 0.001). A significant difference was also observed in gender between the MCI and healthy cognitive groups in urban areas (p = 0.002). Additionally, based on the Kruskal-Wallis test, age showed significant differences between the MCI and healthy cognition groups in urban (p = 0.001) and rural (p = 0.013) areas. Contrary to the urban population, there was a significant association between education (p = 0.02) and economic status (p = 0.012) with MCI in the rural elderly (Table 2).
The logistic regression analysis (Table 3) examined the association between two factors and cognition in older adults. Results showed significant associations between cognition, social relationships, and stress management. Each one-unit increase in the social relationship score corresponded to a 1.12 increase in the odds of healthy cognition (95% CI: 1.06 - 1.19). Similarly, each one-unit increase in the stress management score corresponded to a 1.25 increase in the odds of healthy cognition (95% CI: 1.16 - 1.35).
Discussion
This study aimed to investigate MCI and its determinants in urban and rural areas among older adults. The findings revealed that the MCI prevalence in the total population was 21.9%, which is consistent with findings from a study in 2022 reporting an MCI prevalence of 27% (12). Similarly, another study in America reported an MCI prevalence of 20% (13). Based on the present study's findings, this prevalence was higher in rural areas, possibly due to limited access to healthcare services and educational resources (14).
Another finding of this study was that social relationships were associated with MCI in older adults. Social relationships are crucial in maintaining cognitive function through cognitive stimulation, emotional support, and engagement in stimulating social activities (15,16). Individuals with stronger social networks often have more opportunities for cognitive engagement, such as meaningful conversations and social interactions, which can help preserve cognitive function (17). Additionally, social relationships may buffer against the adverse effects of loneliness (18), a known risk factor for cognitive decline (19).
The findings also indicated an association between stress management and mild cognitive function. Chronic stress has been implicated in cognitive decline through its impact on physiological pathways, including the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system (20). Prolonged activation of these stress-response systems can lead to dysregulation of cortisol levels, neuroinflammation, and oxidative stress, all detrimental to cognitive health (21). Additionally, stress may indirectly affect cognitive function by promoting unhealthy coping behaviors such as smoking, excessive alcohol consumption, and poor dietary habits, which are known risk factors for cognitive impairment (22).
Furthermore, the findings indicated a relationship between MCI and the age of older adults. This association may be explained by various underlying mechanisms inherent to the aging process. Age-related changes in the brain, such as neuronal loss, synaptic dysfunction, and the accumulation of amyloid-beta plaques and tau tangles, contribute to cognitive decline and increase the risk of developing MCI (23). Moreover, aging is often accompanied by other health conditions, such as cardiovascular disease, diabetes, and hypertension, which can further exacerbate cognitive decline (24,25).
Despite the valuable insights provided by this study, several limitations warrant consideration. Firstly, the study's cross-sectional design precludes the establishment of causality between the identified factors and MCI. Additionally, the study relied on self-reported measures for variables such as social relationships and stress management, which may be subject to recall and social desirability biases. Future studies should aim to replicate these findings in diverse populations, including older adults in both marginal and non-marginal areas of cities, to enhance the external validity of the results.
Conclusion
This study elucidates the prevalence and determinants of MCI among older adults in both urban and rural areas of Kermanshah City. Psychosocial factors, such as social relationships and stress management, emerge as crucial in understanding cognitive health in later life. The higher prevalence of MCI in rural areas signals the necessity for targeted interventions addressing the unique challenges in these communities. Additionally, the study highlights that education and economic situation are particularly associated with MCI in older adults residing in rural areas, suggesting that targeted strategies to improve educational and economic conditions could significantly enhance cognitive health in these populations.
Acknowledgement
This article results from a Master's degree dissertation in geriatric nursing. We would like to express our gratitude to the Deputy for Research and Technology at Kermanshah University of Medical Sciences (IR). Additionally, we extend our sincere appreciation to Mr. Sina Sharifi for his invaluable support and assistance throughout the research.
Funding sources
This work received financial support from the Deputy for Research and Technology, Kermanshah University of Medical Sciences (IR) [4010163].
Ethical statement
The study was carried out after obtaining ethical approval from the Research Committee of Kermanshah University of Medical Sciences with code IR.KUMS.REC.1401.565. Prior to participation, all participants were informed about the study objectives and their right to withdraw at any point during the study.
Conflicts of interest
The authors declare no conflicts of interest.
Author contributions
ZR: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Writing - Original draft. LR: Conceptualization, Methodology, Writing - Review and Editing. MR: Conceptualization, Data Curation, Investigation, Methodology, Writing - Review and Editing. NS: Formal Analysis, Writing - Review and Editing.
Due to medical advancements that have increased life expectancy, the proportion of people over 60 is expected to double by 2050, especially in developing countries like Iran (1,2).
Mild Cognitive Impairment (MCI) signifies a critical juncture between the cognitive changes associated with normal aging and the onset of more severe cognitive decline, such as dementia (3). In older adults, experiencing difficulties with information processing and memory retention can significantly impact their ability to carry out daily tasks, ultimately eroding their independence (4).
Cognitive function is influenced by various factors in urban and rural settings (5,6). Urban areas face challenges like pollution, noise, social isolation, and healthcare access that impact cognitive function (7). Conversely, rural areas contend with limited healthcare access, lower education levels, labor-driven activities, and tighter social networks (5).
This study aimed to investigate the MCI status in rural and urban areas while exploring the relationships between factors such as stress management, social and interpersonal relationships, socioeconomic status, and MCI in older adults residing in these diverse environments.
Methods
A cross-sectional study with a descriptive-analytic approach was conducted to meet the study objectives. The study included individuals aged 60 years and older with health records in Kermanshah City's healthcare centers. Inclusion criteria comprised age 60 or older, informed consent, no history of psychotic or cognitive disorders, and no hearing or speech impairments. Exclusion criteria included unwillingness to cooperate or incomplete examination/questionnaire.
The minimum required sample size was calculated to be 460 people based on Bae et al.'s study and the variance of the Mini-Mental State Examination (MMSE) score (8). A 10% sample loss was added to the target volume to ensure more accurate results. Consequently, the target sample size was 506 people.
Multi-stage cluster sampling was used, initially stratifying the sample into urban and rural areas. Kermanshah City was divided into eight clusters based on municipal divisions, including 24 urban areas and 77 health centers. Rural areas were divided into 12 comprehensive rural health service centers and 149 rural health centers, serving as clusters. Cluster selection was based on the population distribution of individuals aged 60 and older, with 17% in rural areas and 83% in urban areas. Twenty clusters were identified, with four from rural and 16 from urban health centers. Samples were randomly chosen within each center after obtaining written consent.
Data Collection
After obtaining the necessary approvals and permits in 2022, the researcher visited health centers in rural and urban Kermanshah armed with an introduction letter. Samples were randomly selected within each center. Eligible individuals were contacted by phone, briefed on the study, and invited to participate. After providing informed consent, the participants completed questionnaires via interviews. Data collection took place from March to September 2023, spanning six months.
Measures
Cognitive function was assessed using MMSE, and the education-specific cutoff points of total MMSE scores were used to identify MCI. The cutoff points for total MMSE scores were ≤ 19 for illiterate individuals, ≤ 22 for participants with high school education, and ≤ 26 for those with a diploma or higher educational level (9). The reliability and validity of the MMSE questionnaire in Iran were established by Seydian et al., who reported a satisfactory Cronbach's alpha coefficient of 0.81 for internal reliability (10).
Stress management and social relationships were evaluated using a scale developed by Ishaghi et al. The scale, originally comprising 46 questions across five domains, was adapted for this study. It assessed stress management with five questions and social relationships with seven questions. Responses ranged from 'very low' to 'very high' on a 5-point Likert scale. Internal correlational coefficients were 0.71 for stress management and 0.81 for social relationships, yielding a total Cronbach's alpha coefficient of 0.76 (11).
Data analyses
Data were analyzed using SPSS version 16, employing the Logistic Regression test, Kruskal-Wallis test, and Mann-Whitney U test due to the non-normal distribution of the data. Univariate logistic regression analyses were conducted to examine the relationship between each independent variable and MCI. Variables were included in the analysis if they had a p-value of less than 0.01 to ensure a high level of statistical rigor. The variables included in the analysis were social relationships, stress management, age, and gender. The estimated indices for the logistic regression analysis included odds ratios and 95% confidence intervals. Statistical significance was set at p < 0.05 for all analyses.
Results
A total of 506 older adults participated, 419 from urban areas and 87 from rural areas in Kermanshah. Among them, 54% were men, and 70% were illiterate, with an 80% illiteracy rate among rural participants. Forty-five percent of the participants were aged between 60 and 65 years old. Details are summarized in Table 1. The overall prevalence of MCI was 21.9%. Urban areas showed a 15% prevalence, while rural areas had a higher prevalence rate of 55%.
Significant differences were found in social relationships between the MCI and healthy cognitive groups in both urban (p = 0.001) and rural (p = 0.04) areas, according to the Mann-Whitney U test. However, stress management significantly differed between the groups only in urban areas (p = 0.001). A significant difference was also observed in gender between the MCI and healthy cognitive groups in urban areas (p = 0.002). Additionally, based on the Kruskal-Wallis test, age showed significant differences between the MCI and healthy cognition groups in urban (p = 0.001) and rural (p = 0.013) areas. Contrary to the urban population, there was a significant association between education (p = 0.02) and economic status (p = 0.012) with MCI in the rural elderly (Table 2).
The logistic regression analysis (Table 3) examined the association between two factors and cognition in older adults. Results showed significant associations between cognition, social relationships, and stress management. Each one-unit increase in the social relationship score corresponded to a 1.12 increase in the odds of healthy cognition (95% CI: 1.06 - 1.19). Similarly, each one-unit increase in the stress management score corresponded to a 1.25 increase in the odds of healthy cognition (95% CI: 1.16 - 1.35).
Discussion
This study aimed to investigate MCI and its determinants in urban and rural areas among older adults. The findings revealed that the MCI prevalence in the total population was 21.9%, which is consistent with findings from a study in 2022 reporting an MCI prevalence of 27% (12). Similarly, another study in America reported an MCI prevalence of 20% (13). Based on the present study's findings, this prevalence was higher in rural areas, possibly due to limited access to healthcare services and educational resources (14).
Another finding of this study was that social relationships were associated with MCI in older adults. Social relationships are crucial in maintaining cognitive function through cognitive stimulation, emotional support, and engagement in stimulating social activities (15,16). Individuals with stronger social networks often have more opportunities for cognitive engagement, such as meaningful conversations and social interactions, which can help preserve cognitive function (17). Additionally, social relationships may buffer against the adverse effects of loneliness (18), a known risk factor for cognitive decline (19).
The findings also indicated an association between stress management and mild cognitive function. Chronic stress has been implicated in cognitive decline through its impact on physiological pathways, including the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system (20). Prolonged activation of these stress-response systems can lead to dysregulation of cortisol levels, neuroinflammation, and oxidative stress, all detrimental to cognitive health (21). Additionally, stress may indirectly affect cognitive function by promoting unhealthy coping behaviors such as smoking, excessive alcohol consumption, and poor dietary habits, which are known risk factors for cognitive impairment (22).
Furthermore, the findings indicated a relationship between MCI and the age of older adults. This association may be explained by various underlying mechanisms inherent to the aging process. Age-related changes in the brain, such as neuronal loss, synaptic dysfunction, and the accumulation of amyloid-beta plaques and tau tangles, contribute to cognitive decline and increase the risk of developing MCI (23). Moreover, aging is often accompanied by other health conditions, such as cardiovascular disease, diabetes, and hypertension, which can further exacerbate cognitive decline (24,25).
Despite the valuable insights provided by this study, several limitations warrant consideration. Firstly, the study's cross-sectional design precludes the establishment of causality between the identified factors and MCI. Additionally, the study relied on self-reported measures for variables such as social relationships and stress management, which may be subject to recall and social desirability biases. Future studies should aim to replicate these findings in diverse populations, including older adults in both marginal and non-marginal areas of cities, to enhance the external validity of the results.
Conclusion
This study elucidates the prevalence and determinants of MCI among older adults in both urban and rural areas of Kermanshah City. Psychosocial factors, such as social relationships and stress management, emerge as crucial in understanding cognitive health in later life. The higher prevalence of MCI in rural areas signals the necessity for targeted interventions addressing the unique challenges in these communities. Additionally, the study highlights that education and economic situation are particularly associated with MCI in older adults residing in rural areas, suggesting that targeted strategies to improve educational and economic conditions could significantly enhance cognitive health in these populations.
Acknowledgement
This article results from a Master's degree dissertation in geriatric nursing. We would like to express our gratitude to the Deputy for Research and Technology at Kermanshah University of Medical Sciences (IR). Additionally, we extend our sincere appreciation to Mr. Sina Sharifi for his invaluable support and assistance throughout the research.
Funding sources
This work received financial support from the Deputy for Research and Technology, Kermanshah University of Medical Sciences (IR) [4010163].
Ethical statement
The study was carried out after obtaining ethical approval from the Research Committee of Kermanshah University of Medical Sciences with code IR.KUMS.REC.1401.565. Prior to participation, all participants were informed about the study objectives and their right to withdraw at any point during the study.
Conflicts of interest
The authors declare no conflicts of interest.
Author contributions
ZR: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Writing - Original draft. LR: Conceptualization, Methodology, Writing - Review and Editing. MR: Conceptualization, Data Curation, Investigation, Methodology, Writing - Review and Editing. NS: Formal Analysis, Writing - Review and Editing.
perspective: Original Article |
Subject:
Psychology and Psychiatry
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