Response letter (Response to Reviewers)
PONE-D-20-03550: Cognitive impairment and its risk factors among Myanmar elderly using
the Revised Hasegawa’s Dementia Scale: A cross-sectional study in Nay Pyi Taw, Myanmar
Thank you very much editor and reviewer for your valuable comments and suggestions.
We have revised the manuscript according to your suggestions. The revised and edited
sentences (and words) are mentioned using a track-changes function in the revised
manuscript. We also submitted a clean version of revised manuscript as a separate
file. In below responses, we noted reviewer’s comments in black color and our responses
in blue color.
Reviewer #1:
Summary: The current manuscript investigated the prevalence of cognitive impairment
and related comorbidities among Myanmar elderly. The authors show that the prevalence
of impaired cognitive functions among participants was from 23% in males to 32 % in
females. Authors also evidence that cognitive impairment was associated with age,
illiterate, and general health. Although authors present exciting findings and I would
read the final manuscripts, some aspects could be improved. Please consider the following
suggestions for revision:
Q-1: Introduction: Overall, the introduction provides a broad background and rationale
for the research. However, it lacks information on the area of the background relative
to the numerous socio-demographic, physical, and mental conditions associated with
cognitive impairment. Moreover, the rationale regarding the choice to use the revised
Hasegawa's dementia scales for the assessment of cognitive impairment is not clear.
More studies evidence that this general test does not always lead to a clear definition
of the prevalence. Therefore, it would be appropriate to justify the choice. Finally,
the researchers' hypotheses are unclear.
A-1: Authors’ response: Thank you very much for the comments. As suggested, the information
on the area of background relative to the numerous socio-demographic, physical, and
mental conditions associated with cognitive impairment were added in the introduction
section. The previous second paragraph in the introduction section, “Numerous socio-demographic,
physical, and mental conditions have been found to be associated with cognitive impairment.
Older age [8,9], being female [10-12], poor marital relationship [10,13-15], low educational
level in earlier life [16,17], solitary living [10,13-15], low level of physical activities
[10,18-21], underweight [10,22], hypertension [23], and diabetes mellitus [24] are
important risk factors for cognitive decline. Meanwhile, high socio-economic status
[25,26]; high level of social activities [13,27]; good nutrition [28]; being free
from anxiety, stress [14], or depression [15]; as well as high level of physical activities
[18–22] have been observed to be protective factors against cognitive impairment.”
is revised and read as follows: “Numerous socio-demographic, physical, and mental
conditions have been found to be associated with cognitive impairment. Older age [8,9],
being female [10-12], poor marital relationship [10,13-15], low educational level
in earlier life [16,17], solitary living [10,13-15], low level of physical activity
[10,18-21], chronic tobacco smoking [22], alcohol consumption [23], obesity [24,25],
visual impairment [26], hypertension [27], and diabetes mellitus [28,29] are important
risk factors for cognitive decline. Meanwhile, high socioeconomic status [30,31];
high level of social activities [13,32]; good nutrition [33]; being free from anxiety,
stress [14], or depression [15]; as well as high level of physical activity [18–21]
have been observed to be protective factors against cognitive impairment.
Age and gender are unmodifiable risk factors for cognitive decline. In the normal
aging process, brain volume shrinkage, especially in the prefrontal cortex, which
is responsible for memory performance, starts after 40 years of age and a rapid decrease
in brain volume has been observed in patients over 70 years of age [34]. Nowadays,
the world’s population is aging as advanced medical technological advances increase
life expectancy, and age-related cognitive declination has become a major issue. Non-communicable
diseases (NCDs) such as hypertension, diabetes mellitus, and obesity due to low physical
activity accompany aging [24,25,29,35]. These are responsible for rapid brain aging
and cerebral-vascular accidents, provoking the action of pro-inflammatory cytokines
with the resultant chronic inflammation and cerebral white matter atrophy leading
to cognitive impairment [24,25,34]. Cognitive impairment is also influenced by hormonal
changes, and females suffer most, especially after menopause, due to decreased estrogen
levels [10,12].
Learning or education, especially in childhood, enhances brain structure and development
by increasing brain vascularization, synapse number, and connections, which improve
cognitive function [36,37]. Higher education levels are associated with lower cognitive
decline as learning creates favorable structures and neurochemical alterations in
the brain [36,37]. High socioeconomic status, high physical and social activities,
and less dependency are protective factors for cognitive impairments [13,30-32]. People
with high socioeconomic status generally have more social contact and activities that
make them more active, less dependent, and perform higher physical activities leading
to slower cognitive decline [13,30-32]. Moreover, these people can have good nutrition
and can easily access the health services they need, maintaining their health in a
good state that can delay cognitive declination [30-32].
On the contrary, elderly people living a solitary life and with failed marital status
or unhealthy behaviors such as chronic smoking or alcohol consumption had a higher
risk of developing cognitive impairments [10,13,15,22]. Elderly individuals who live
alone and are widowed, divorced, or separated may have low social contact and activities
that can initiate or exacerbate lower mood or depression, the high-risk factor for
cognitive impairment [10,13-15]. Elderly people leading a lonely life may also harbor
risky behaviors such as chronic alcohol drinking or smoking, as there is no family
member to control them, which can increase cognitive impairment [10,13,15,22].” [Introduction,
Line 63-102, Page 3-5]
Moreover, the rationale regarding the choice to use the revised Hasegawa’s dementia
scale for the assessment of cognitive impairment was also added in the introduction
section of the revised manuscript. The last paragraph of the previous introduction
section “Few studies have been performed on the cognitive function assessment among
elderly in Myanmar. The age- and sex-specific cognitive functions, as well as the
influencing factors, are not yet reported. This kind of information is extremely useful
to the planning of programs for elderly care, treatment, and prevention of dementia
strategies. Therefore, the present study aimed to identify the rate of cognitive impairment
and related comorbidities among Myanmar elderly.” was revised as follows: “Few studies
have been performed on the cognitive function assessment among elderly in Myanmar.
Assessing cognitive impairment among the Myanmar elderly using the Myanmar-translated
version of the Revised Hasegawa’s Dementia Scale (HSR-D) is still lacking. The HSR-D
Myanmar version was officially translated by Myanmar and Japanese scientists, and
some modifications were made according to the local context [41]. The scale does not
include questions assessing the reading and writing ability of the respondents, making
it convenient to use for illiterate people and every ethnic group using different
languages in Myanmar. Therefore, it can be used as a screening tool to easily detect
cognitive impairment among communities even by the basic health staff, which could
be quite helpful in Myanmar with limited human resources for health. Age- and sex-specific
cognitive functions, as well as the influencing factors, have not yet been reported.
This kind of information is extremely useful to plan programs for elderly care, treatment,
and prevention of dementia strategies. Therefore, the present study aimed to identify
the rate of cognitive impairment and its risk factors among the elderly in Myanmar.”
[Introduction, Line 114-127, Page 5-6]
To reflect the reviewer’s comments, the previous usage “the prevalence of the cognitive
impairment” revised and read as follows: “the rate of cognitive impairment” throughout
the revised manuscript.
Q-2: Methods: The method is succinct and comprehensive. Analysis: the analyses are
well conducted. However, it could be useful to define the confounding variables controlled
with AORs.
A-2: Authors’ response: Thank you very much for your valuable suggestion. As suggested,
we added the definition of confounding variables one of the sub-section of methods
“Independent variables
Socio-demographic characteristics, substance use behaviors, and health problems were
considered as independent variables. The current age was categorized into three groups
(60-69, 70-79, and ≥80) based on the 10-year age intervals. Marital status was categorized
into three groups (single, married, and separated/divorced/windowed). Education was
divided into three groups according to the educational background of respondents of
the elderly: middle school and above, primary school, and only read and write, and
illiterate. Family type was categorized into five groups: living alone, nuclear, extended,
three generations, and skip generation to learn how family structures affect the cognitive
functions of the elderly.
Substance use behaviors were grouped into the following categories: non-users (never
use), ex-users, occasional users, and daily users to see the effect on the cognitive
functions of respondents. Self-rated health, physical activities, and vision status
were divided into two categories. The nutritional status of the elderly may play an
important role in impairment of cognitive function. Therefore, Body mass index (BMI)
was categorized as underweight, normal, overweight, and obese. Hypertension and diabetes
mellitus were grouped into two categories according to self-reported and measurement
results. The measurement cutoff point of blood pressure was 140/90 mmHg (hypertension:
≥140/90 mmHg) and random blood sugar was 200 mg/dL (diabetes mellitus: ≥ 200 mg/dL).”
[Methods, Line 114-127, Page 7-8]
In addition, we also amended the previous footnote of Table 4 “Adjusted for the variables
listed in the table.” to read as follows: “Adjusted for age, gender, marital status,
education, dependent, family type, alcohol, smoking and smokeless tobacco use, self-rated
health, no. of” comorbidity, low physical activities, vision status, BMI, hypertension,
and diabetes mellitus. [Results-Table 4, Line 33-36, Page 16]
Q-3: Results: The summary of the study provided is well-defined and fits according
to the analysis plan provided. Could it be useful for reporting X2 in the table?
A-3: Authors’ response: Thank you very much for your valuable suggestion. As suggested,
we reported Pearson's chi-square test value in the table 1 and 2. [Results Table 1
and 2, Line 217 and 230, Page 11-12]
Table 1 Socio-demographic characteristic of participants
Characteristics Total (N=757) Male (n=246) Female (n=511) X2 ‡
N % n % n %
Age 15.01*
60-64 203 26.8 51 20.7 152 29.7
65-69 185 24.4 63 25.6 122 23.9
70-74 170 22.5 65 24.4 105 20.5
75-79 95 12.5 24 9.8 71 13.9
80-84 68 9.0 26 106 42 8.3
85-89 27 3.6 12 4.9 15 2.9
≥ 90 9 1.2 5 2.0 4 0.8
Education 88.91***
Illiterate 165 21.8 11 4.5 154 30.1
Primary school 487 64.3 173 70.3 314 61.4
Middle school 72 9.5 41 16.7 31 6.1
High school 22 2.9 16 6.5 6 1.2
University and above 11 1.5 5 20.0 6 1.2
Marital status 75.94***
Single 49 6.5 13 5.3 36 7.0
Married 393 51.9 183 74.4 210 41.1
Others 315 41.6 50 20.3 265 51.9
Family type 12.43*
Living alone 56 7.5 10 4.1 46 9.0
Nuclear 108 14.3 47 19.1 61 11.9
Extended 438 57.8 144 58.5 294 57.5
Three generation 113 14.9 32 13.0 81 15.9
Skip generation 42 5.5 13 5.3 29 5.7
Low physical activities 4.50*
No 232 69.4 158 64.2 367 71.8
Yes 525 30.6 88 35.8 144 28.2
Place of interview 10.33
Ngan Sat RHC 60 7.9 26 10.6 34 6.7
Tha Pyay Pin RHC 97 12.8 26 10.6 71 13.9
Zee Kone RHC 81 10.7 29 11.8 52 10.2
Nat Tha Ye RHC 92 12.2 30 12.2 62 12.1
Taung Po Thar RHC 54 7.2 18 7.3 36 7.0
Ma Dot Pin RHC 45 5.9 12 4.9 33 6.5
Baw Di Gone RHC 60 7.9 19 7.7 41 8.0
Tha Wut Hti RHC 70 9.2 27 11.0 43 8.4
Nyaung Lont RHC 87 11.5 31 12.6 56 11.0
Pyi San Aung RHC 49 6.5 11 4.5 38 7.4
Si Pin Thar Yar RHC 62 8.2 17 6.8 45 8.8
RHC: Rural Health Center. ‡Pearson's chi-square test. *p<0.05, **p<0.01, ***p<0.001
Table 2 Substance use behaviors and health-related characteristic of participants
Characteristics Total (N=757) Male (n=246) Female (n=511) X2‡
N % n % n %
Smoking 50.69***
Never smoke 516 68.2 125 50.8 391 76.5
Ex-smoker 102 13.5 50 20.3 52 10.2
Occasional smoker 34 4.5 17 6.9 17 3.3
Daily smoker 105 13.8 54 22.0 51 10.0
Smokeless tobacco use 9.40*
Never use 457 60.4 139 56.5 318 62.2
Ex-user 27 3.6 15 6.1 12 2.3
Occasional user 76 10.0 21 8.5 55 10.8
Daily user 197 26.0 71 28.9 126 24.7
Alcohol drinking 119.37***
Never Drink 684 90.4 181 73.6 503 98.4
Ex-drinker 52 6.9 44 17.9 8 1.6
Occasional/social drinker 17 2.2 17 6.9 0 0
Heavy drinker 4 0.5 4 1.6 0 0
Self-rated health 4.50
Very poor/poor 267 35.3 76 30.9 191 37.4
Fair 237 31.3 76 30.9 161 31.5
Good/very good 253 33.4 94 38.2 159 31.1
Comorbidity 5.81
No disease 151 19.9 54 22.0 97 19.0
At least one disease 342 45.2 121 49.2 221 43.2
Two or more diseases 264 34.9 71 28.8 193 37.8
Vision status 2.74
Good 371 49.0 128 52.0 243 47.6
Fair 331 43.7 105 42.7 226 44.2
Poor 55 7.3 13 5.3 42 8.2
BMI 5.72
Under weight (11.9-18.4) 220 29.1 69 28.0 151 29.5
Normal (18.5-22.9) 283 37.3 106 43.1 177 34.6
Overweight (23.0-24.9) 106 14.0 29 11.8 77 15.2
Obese (=>25) 148 19.6 42 17.1 106 20.7
Hypertension 7.75*
No 297 39.2 79 32.1 218 42.7
Yes 460 60.8 167 67.9 293 57.3
Diabetes mellitus 0.20
No 599 79.1 197 80.1 402 78.7
Yes 158 20.9 49 19.9 109 21.3
‡Pearson's chi-square test. *p<0.05, **p<0.01, ***p<0.001
Q-4: Discussion: This section could be improved, also introducing the specific comments
included below. Some explanations about brain change in the elderly could be helpful
to define better why the elderly are more vulnerable to cognitive impairment.
A-4: Authors’ response: Thank you very much for the comments. As suggested, we revised
the pervious sentences “In this study, the participants who were older than 70 years
had a higher risk for developing cognitive impairment compared with the 60–69 years
old age group. This finding is consistent with other studies [8,9,23,24]. The rate
of cognitive impairment is the highest in the age group of 85 years and older, ranging
from 16.7% in China [8] to 43% in Germany [9]. Studies have also estimated that elderly
aged 75 years or older account for 80% of patients with dementia [23,24]. Older age
is the greatest unmodifiable risk factor for cognitive impairment [16], and should
be taken into consideration in cognitive impairment prevention, as life expectancy
is increasing worldwide [16]. Aging can also combine with other comorbidities and
can exacerbate the situation [16]. As such, health policymakers and stakeholders in
Myanmar should initiate community preventive measures or strategies for cognitive
impairment as early as possible to mitigate its adverse consequences.” to add more
explanations about brain change in the elderly and read as follows: “In this study,
the participants older than 70 years had a higher risk of developing cognitive impairment
compared with the 60–69 years old age group. This finding is consistent with those
of other studies [8,9,27,28]. The rate of cognitive impairment is the highest in the
age group of 85 years and older, ranging from 16.7% in China [8] to 43% in Germany
[9]. Studies have also estimated that elderly aged 75 years or older account for 80%
of patients with dementia [27,28]. Aging is associated with several changes in brain
structure and function. Brain volume shrinkage started around or after 40 years, and
the shrinkage rate increased especially for those over 70, even in the normal aging
process [34]. The most affected area is the prefrontal cortex, which is responsible
for memory performance. Reduction in cortical volume associated with increased white
matter lesions in the elderly leads to executive function declination and cognitive
impairment [34]. Moreover, aging is usually associated with NCDs such as hypertension
and type 2 diabetes mellitus [29,35]. Hypertension and diabetes mellitus account for
small or large vascular changes leading to cerebral-vascular accidents, strokes, cerebral
hemorrhage or micro-cerebral infarcts. As a result, cognitive function impairment
is inevitable due to brain volume reduction [29,34,35]. Older age is the greatest
unmodifiable risk factor for cognitive impairment [16], and should be taken into consideration
in cognitive impairment prevention, as life expectancy is increasing worldwide. Aging
can also combine with other comorbidities and can exacerbate the situation [16]. As
such, health policymakers and stakeholders in Myanmar should initiate community preventive
measures or strategies for cognitive impairment as early as possible to mitigate its
adverse consequences.” [Discussion, Line 287-307, Page 17]
Furthermore, the brain changes associated with obesity (BMI and cognitive impairment)
was mentioned in the revised paragraph of discussion section as follows: “Moreover,
middle-age obesity is more likely to be associated with rapid brain ageing and cerebral
white matter atrophy due to the action of pro-inflammatory cytokines causing chronic
inflammation and metabolic diseases [23,24,32].” [Discussion, Line 377-379, Page 20]
Q-5: The explanation about BMI is not correct. Recent studies reported a higher risk
of occurrence of cognitive impairment in individuals with excessive body weight and
high BMI; it could be useful to report these studies and hypothesize a different explanation
for this (e.g., non-linear relationship). It is reporting these results to provide
a complete view.
Some aspects could be discussed, such as hypertension, behavioral risk factors (alcohol,
cigarettes, etc.), and diabetes. All of these are considered as risk factors for cognitive
impairment from a lot of studies (and systematic reviews). In particular, to consider
that in all samples, 57% of females and 67% of the male are hypertensive.
I suggest the reading of the following systematic review:
“Favieri, F., Forte, G. & Casagrande M. (2019). The executive functions in overweight
and obesity: a systematic review of neuropsychological cross-sectional and longitudinal
studies. Frontiers in Psychology, 10, 1-27, DOI: 10.3389/fpsyg.2019.02126”;
“Forte, G., De Pascalis, V., Favieri, F., & Casagrande, M. (2020). Effects of Blood
Pressure on Cognitive Performance: A Systematic Review. Journal of Clinical Medicine,
9(1), 34” ;
“Cuevas, H. E. (2019). Type 2 diabetes and cognitive dysfunction in minorities: a
review of the literature. Ethnicity & health, 24(5), 512-526.”
“Conti, A. A., McLean, L., Tolomeo, S., Steele, J. D., & Baldacchino, A. (2019). Chronic
tobacco smoking and neuropsychological impairments: A systematic review and meta-analysis.
Neuroscience & Biobehavioral Reviews, 96, 143-154.”
“Dye, L., Boyle, N. B., Champ, C., & Lawton, C. (2017). The relationship between obesity
and cognitive health and decline. Proceedings of the nutrition society, 76(4), 443-454”.
A-5: Authors’ response: Thank you for your comments. The systematic reviews mentioned
by the reviewer were thoroughly read and cited in the revised manuscript. As suggested,
the previous paragraph explaining about obesity in the discussion section “Among all
participants, those whose BMI was in the normal or obese category were observed to
have a lower risk for developing cognitive impairment. A high BMI reflects good nutrition
[40] as well as high body fat, which is favorable for increased glucose metabolism,
especially cerebral glucose metabolism, which enhanced cognitive performance [41,42].
Studies have also observed that individuals with a low BMI have a higher risk for
developing cognitive impairment compared with those with a high BMI [10,22].” was
revised and some aspects regarding hypertension, behavioral risk factors (alcohol,
cigarettes, etc.), and diabetes were added and to read as follows: “Participants whose
BMI was in the normal or obese category were observed to have a lower risk of developing
cognitive impairment in this study. Many studies agree that maintaining normal body
weight throughout the life span is considered to be a protective factor for cognitive
impairment, which is consistent with this study [24,25]. Increased BMI associated
with central obesity in the middle years of life has been observed as one of the risk
factors for cognitive decline in older age by recent systematic reviews and meta-analyses
[24,25]. Obesity in the middle years of life is usually related to hypertension, stroke,
diabetes mellitus, and dyslipidemia [24,25,29,35]. Moreover, middle-age obesity is
more likely to be associated with rapid brain aging and cerebral white matter atrophy
due to the action of pro-inflammatory cytokines causing chronic inflammation and metabolic
diseases [24,25,34]. Pathophysiological changes in obesity are considered to be related
mainly to adiposity distribution, which cannot be measured directly by BMI as it fails
to differentiate muscles from adipose tissues [24,25]. This study used BMI to categorize
underweight, normal, overweight, and obese among the participants. This is one of
the possible reasons why obese participants in this study had a lower risk of cognitive
impairment. Another reason for the lower risk of cognitive impairment among obese
participants in this study is that their obesity may start in their late-life as obesity
in the later years of life (over 76 years of age) is found to be associated with slower
cognitive decline in some studies [24,25].
In this study, 13.8% of the participants were daily smokers and 0.5% were heavy drinkers.
Chronic smokers were more likely to be alcohol drinkers, and chronic tobacco smoking
is associated with cognitive decline and the development of neurocognitive diseases
in later life [22]. However, this association was not found in this study. This may
be due to the limited number of study participants and the study conducted in the
same demographic area in Myanmar.” [Discussion, Line 370-393, Page 20-21]
Q-6: Regarding limitations, it could be useful reporting the percentage of participants
in whom an impairment has been confirmed. Conclusion: the conclusions appear to be
a summary of the results, I suggest reporting the usefulness of this study and further
perspective.
A-6: Authors’ response: Thank you very much for your comments. As suggested, the percentage
of the participants with cognitive impairment was reported in the limitations. The
previous limitations sentence, “This study has a number of strengths and limitations.
This is the first study to investigate the rate of impaired cognitive function using
the Myanmar-translated version of HDS-R and its related comorbidities among Myanmar
elderly. HDS-R has its own advantages. As it does not include questions assessing
the reading and writing ability of respondents, it is convenient to use in illiterate
people or in minor ethnic groups who cannot read and write the Myanmar language. Cognitive
impairment could be checked easily by basic health staff using HDS-R. However, cognitive
impairment among the participants in this study was not confirmed by a psychiatrist.
The study was conducted in one region in the central part of Myanmar, which limited
the generalizability of the results, given that different socio-demographic features
are represented across Myanmar. The causal relation between cognitive impairment and
different risk factors could not be explored clearly owing to the cross-sectional
design of the study.” was revised as follows: “This study has several strengths and
limitations. This is the first study to investigate the prevalence of impaired cognitive
function using the Myanmar-translated version of the HDS-R and its related comorbidities
among Myanmar elderly. It was observed that 23.6% of males and 32.9% of females in
this study had cognitive impairment detected by the HDS-R. The HDS-R has its own advantages.
As it does not include questions assessing the reading and writing ability of respondents,
it is convenient to use in illiterate people or in minor ethnic groups who cannot
read and write the Myanmar language. Cognitive impairment could be checked easily
by basic health staff using the HDS-R. However, cognitive impairment among the participants
in this study was not confirmed by a psychiatrist; therefore, cognitive impairment
in specific domains such as executive function, spatial working memory, processing
speed, attention, verbal memory, or verbal fluency could not be ruled out. The study
was conducted in one region in the central part of Myanmar, which limited the generalizability
of the results, given that different socio-demographic features are represented across
Myanmar. The causal relationship between cognitive impairment and different risk factors
could not be explored clearly owing to the cross-sectional design of the study.” [Discussion,
Line 394-408, Page 21]
According to the reviewer’s comments, the usefulness of this study and further perspective
was reported in the conclusion of the revised manuscript. The previous paragraph in
the conclusion section “About 30% of the participants had cognitive impairment and
female participants were significantly more likely to develop cognitive impairment
compared to male. Being over 70 years old, a low educational level, dependency, solitary
living, and poor self-rated health were associated with a higher risk for developing
cognitive impairment. Meanwhile, living with one’s family and good self-rated health
were protective factors. Policymakers need to consider implementing community preventive
measures or strategies regarding cognitive impairment as early as possible to mitigate
its adverse consequences.” was revised as follows: “Three out of five elderly participants
reported having cognitive impairment and female participants were significantly more
likely to develop cognitive impairment. Being over 70 years old, a low educational
level, dependency, solitary living, and poor self-rated health were associated with
a higher risk of developing cognitive impairment. Meanwhile, living with one’s family
and having good self-rated health were protective factors. Based on scientific evidence,
policymakers need to consider implementing community preventive measures or strategies
regarding cognitive impairment and gender differences as early as possible to mitigate
its adverse consequences among Myanmar elderly. Screening for cognitive impairment
using the Myanmar language version of the HDS-R should be confirmed by the clinical
diagnosis in the further studies so that even the basic health staff can screen for
cognitive impairment among the general population at the most basic lever. Therefore,
it could be helpful in limited health workforce settings. Future research should be
performed not only to detect general cognitive impairment but also to differentiate
specific cognitive domains impairments among the Myanmar elderly. Longitudinal studies
are needed to observe the causal and protective factors associated with cognitive
impairments and associated comorbidities in Myanmar.” [Conclusion: Line 410-424, Page
22]
Furthermore, the previous conclusion section in the abstract “Conclusion: About 30%
of participants had cognitive impairment, and female participants were significantly
more likely to develop cognitive impairment compared to male participants. Being over
70 years old, having a low educational level, dependency, solitary living, and poor
self-rated health were associated with a higher risk for cognitive impairment. Meanwhile,
living with families and self-reported good health were protective factors against
cognitive impairment.” was also revised into as follows: “Conclusion: Using the HDS-R
Myanmar version, this study reported that there out of five elderly participants had
cognitive impairment, and its risk factors, altering policy makers that Myanmar needs
to prepare for adequate healthcare services and social support for elderly with cognitive
impairment. Future research should be performed not only to detect general cognitive
impairment but also to differentiate specific cognitive domains impairments among
Myanmar elderly. Longitudinal studies are needed to observe the causal and protective
factors associated with cognitive impairments in Myanmar.” [Abstract, Line 44-50,
Page 2]
Q-7: General comment: I would also encourage the authors to check all references and
to proofread the manuscript to improve the English language.
A-7: Authors’ response: Thank you very much for your comments. As suggested, we checked
all the references and revised it accordingly. Our manuscript is proofreader by the
professional proofreaders with public health experience to improve the language quality
of the revised manuscript.
Reviewer #2:
Q-1: p. 4 - bottom: clarify what you mean by ‘unable to sum the value’.
A-1: Authors’ response: Thank you for your question. We corrected the previous word
‘unable to sum the value’ into ‘unable to subtract the value’. We also revised the
previous sentence from Materials and Methods section “Elderly individuals who lived
in the study area less than six months, who were diagnosed with cognitive impairments
along with mental and physical disorders (seriously ill), and who did not understand
the Myanmar language and were unable to sum the value were excluded from the study.”
to read as follows: “Elderly individuals who lived in the study area less than six
months, who were diagnosed with cognitive impairments along with mental and physical
disorders (seriously ill), and who did not understand the Myanmar language and were
unable to subtract the value were excluded from the study.” [Methods, Line 135-138,
Page 6]
Q-2: P. 5: please identify how many people were approached to participate in the study,
and whether people who refused participation were different from individuals who agreed
to participate.
A-2: Authors’ response: In total, 971 elderly people approached to participate in
this study. Elderly individuals who lived in the study area less than six months,
who were diagnosed with cognitive impairments along with mental and physical disorders
(seriously ill), and who did not understand the Myanmar language and were unable to
subtract the value were excluded. As the data collection was collected at rural health
centers, the elderly people who would like to voluntarily participate this study came
to the rural health centers. The elderly who lived quite far from rural health centers
with transportation difficulties, those who were out of town at the time of data collection,
and those who had to attend their personal or familial events at the time of data
collection refused to participate in this study. As suggested, we added response
rate of the survey as follows: “In total, 971 elderly people were invited to participate
in this survey. Of them, 811 elderly participants (males: 264 [32.6%], females: 547
[67.4%]) provided written informed consent and agreed to participate in this study.
The response rate was 92.5%.” [Methods, Line 150-155, Page 7]
Q-3: Please provide a theoretical justification for choosing the covariates set. Explain
why you chose the existing set of covariates instead of other covariates.
A-3: Authors’ response: Thanks for your suggestions. We chose the covariates in our
study after thorough literature reviews reporting the risk factors for cognitive impairment,
and found those covariates were applicable to study design, local context and the
community setting of this study. As suggested, the theoretical justification for choosing
the covariates set was mentioned in the revised manuscript as follows: “Numerous socio-demographic,
physical, and mental conditions have been found to be associated with cognitive impairment.
Older age [8,9], being female [10-12], poor marital relationship [10,13-15], low educational
level in earlier life [16,17], solitary living [10,13-15], low level of physical activity
[10,18-21], chronic tobacco smoking [22], alcohol consumption [23], obesity [24,25],
visual impairment [26], hypertension [27], and diabetes mellitus [28,29] are important
risk factors for cognitive decline. Meanwhile, high socioeconomic status [30,31];
high level of social activities [13,32]; good nutrition [33]; being free from anxiety,
stress [14], or depression [15]; as well as high level of physical activity [18–21]
have been observed to be protective factors against cognitive impairment.
Age and gender are unmodifiable risk factors for cognitive decline. In the normal
aging process, brain volume shrinkage, especially in the prefrontal cortex, which
is responsible for memory performance, starts after 40 years of age and a rapid decrease
in brain volume has been observed in patients over 70 years of age [34]. Nowadays,
the world’s population is aging as advanced medical technological advances increase
life expectancy, and age-related cognitive declination has become a major issue. Non-communicable
diseases (NCDs) such as hypertension, diabetes mellitus, and obesity due to low physical
activity accompany aging [24,25,29,35]. These are responsible for rapid brain aging
and cerebral-vascular accidents, provoking the action of pro-inflammatory cytokines
with the resultant chronic inflammation and cerebral white matter atrophy leading
to cognitive impairment [24,25,34]. Cognitive impairment is also influenced by hormonal
changes, and females suffer most, especially after menopause, due to decreased estrogen
levels [10,12].
Learning or education, especially in childhood, enhances brain structure and development
by increasing brain vascularization, synapse number, and connections, which improve
cognitive function [36,37]. Higher education levels are associated with lower cognitive
decline as learning creates favorable structures and neurochemical alterations in
the brain [36,37]. High socioeconomic status, high physical and social activities,
and less dependency are protective factors for cognitive impairments [13,30-32]. People
with high socioeconomic status generally have more social contact and activities that
make them more active, less dependent, and perform higher physical activities leading
to slower cognitive decline [13,30-32]. Moreover, these people can have good nutrition
and can easily access the health services they need, maintaining their health in a
good state that can delay cognitive declination [30-32].
On the contrary, elderly people living a solitary life and with failed marital status
or unhealthy behaviors such as chronic smoking or alcohol consumption had a higher
risk of developing cognitive impairments [10,13,15,22]. Elderly individuals who live
alone and are widowed, divorced, or separated may have low social contact and activities
that can initiate or exacerbate lower mood or depression, the high-risk factor for
cognitive impairment [10,13-15]. Elderly people leading a lonely life may also harbor
risky behaviors such as chronic alcohol drinking or smoking, as there is no family
member to control them, which can increase cognitive impairment [10,13,15,22].” [Introduction,
Line 63-102, Page 3-5]
Q-4: Justify the decision to stratify the HDS-R instead of treating it as a continuous
variable.
A-4: Authors’ response: Thank you for your question. We treated HDS-R as continuous
variable because of the following literature, which stating as follows: “The most
common application of the HDS-R is its use as a screening test for dementia. Using
the cut-off point of 20/21, we obtained the sensitivity of 0.90 and the specificity
of 0.82 in our subject. Those optimum sensitivity and specificity were achieved by
regarding a score of 20 or less as suggestive of dementia”. Reference: Imai Y, Hasegawa
K. The revised Hasegawa’s Dementia Scale (HDS-R)- Evaluation of its usefulness as
a screening test for dementia. J Hong Kong Coll Psychiatr. 1994;4:20-24. Available:
https://easap.asia/index.php/find-issues/past-issue/item/503-v4n2-9402-p20-24.
Q-5: Cognitive function and impairment can differ across various domains such as working
memory, executive function, psychomotor speed, etc. In a study to identify factors
associated with cognitive impairment, the examination of specific cognitive domains
should be essential. The omission of such an examination is a limitation of a study
that purports to seek out risk factors for cognitive impairment.
A-5: Authors’ response: Thank you very much for your comments. As suggested, the examination
of specific cognitive domains should be essential in a study to identify factors associated
with cognitive impairment but this study was limited to do so. Therefore, this limitation
was added and the previous paragraph of limitation “This study has a number of strengths
and limitations. This is the first study to investigate the rate of impaired cognitive
function using the Myanmar-translated version of HDS-R and its related comorbidities
among Myanmar elderly. HDS-R has its own advantages. As it does not include questions
assessing the reading and writing ability of respondents, it is convenient to use
in illiterate people or in minor ethnic groups who cannot read and write the Myanmar
language. Cognitive impairment could be checked easily by basic health staff using
HDS-R. However, cognitive impairment among the participants in this study was not
confirmed by a psychiatrist. The study was conducted in one region in the central
part of Myanmar, which limited the generalizability of the results, given that different
socio-demographic features are represented across Myanmar. The causal relation between
cognitive impairment and different risk factors could not be explored clearly owing
to the cross-sectional design of the study.” was revised to read as follows: “This
study has several strengths and limitations. This is the first study to investigate
the prevalence of impaired cognitive function using the Myanmar-translated version
of the HDS-R and its related comorbidities among Myanmar elderly. It was observed
that 23.6% of males and 32.9% of females in this study had cognitive impairment detected
by the HDS-R. The HDS-R has its own advantages. As it does not include questions assessing
the reading and writing ability of respondents, it is convenient to use in illiterate
people or in minor ethnic groups who cannot read and write the Myanmar language. Cognitive
impairment could be checked easily by basic health staff using the HDS-R. However,
cognitive impairment among the participants in this study was not confirmed by a psychiatrist;
therefore, cognitive impairment in specific domains such as executive function, spatial
working memory, processing speed, attention, verbal memory, or verbal fluency could
not be ruled out. The study was conducted in one region in the central part of Myanmar,
which limited the generalizability of the results, given that different socio-demographic
features are represented across Myanmar. The causal relationship between cognitive
impairment and different risk factors could not be explored clearly owing to the cross-sectional
design of the study.” [Discussion, Line 394-408, Page 21]
Q-6: Ethical considerations: please explain how you ensured that study participants
with cognitive impairment were capable of providing informed consent.
A-6: Authors’ response: Authors’ response: Thank you for your question. If the patient
is deemed incompetent to consent, consent based on legal proxies or advance directives
were obtained. We revised the previous sentence from Methods section, “Research team
members helped illiterate participants read the informed consent form. These participants
were requested to mark their fingerprint if they understood the content of the informed
consent form and agreed to participate in the study.” to read as follows: “Research
team members helped illiterate participants read the informed consent form. These
participants were requested to mark their fingerprint if they understood the content
of the informed consent form and agreed to participate in the study. If participants
were incompetent to consent, consent was taken from their legal proxies or advance
directives.” [Methods, Line 200-204, Page 9]
Q-7: The findings are not novel or surprising, and the choice of variables was not
anchored in any sort of theory. As such, the article seemed to be a fishing expedition
to find statistically significant results. This is a problem because the wide confidence
intervals in the regression analyses suggest the study was underpowered to detect
certain effects.
A-7: Authors’ response: Thank you for your comment. I think you are pointing out the
education variable confidence intervals in the regression analyses for UOR results
“Illiterate (UOR = 14.2; 95% CI: 6.45–31.08) and AOR results “Illiterate (AOR = 9.1;
95% CI: 3.82–21.51)”. Following your suggestion, we categorized education variable
to two categories (Middle school and above vs. Only read and write/primary school/illiterate)
instead of three (Middle school and above, Only read and write/primary school, and
Illiterate) and re-analyzed it. We found that the confidence intervals in the regression
analyses becomes narrow: UOR results “Illiterate (UOR = 6.1; 95% CI: 2.91–12.75) and
AOR results “Illiterate (AOR = 3.9; 95% CI: 1.77–8.42)”. The re-categorization of
variable hasn’t effect the results of other variables. Other AOR results are remained
the similar to our pervious analysis. Please refer below “Re-analysis Table 4”
Re-analysis Table 4 Multivariable logistic regression analysis of factors associated
with cognitive impairment among Myanmar elderly (N=757)
Characteristics OR 95% CI AOR† 95% CI
Age
60-69
70-79 2.3 (1.63-3.33)*** 1.8 (1.19-2.70)**
≥80 4.9 (3.07-7.72)*** 3.9 (2.25-6.76)***
Gender
Male
Female 1.6 (1.12-2.25)** 1.1 (0.69-1.73)
Marital status
Single
Married 1.1 (0.53-2.31) 1.1 (0.48-2.46)
Separated/Divorced/Windowed 2.7 (1.30-5.61)** 1.4 (0.63-3.27)
Education
Middle school and above
Only read and write /Primary school 4.4 (2.07-9.24)*** 3.4 (1.56-7.52)**
Illiterate 14.2 (6.45-31.08)*** 9.1 (3.82-21.51)***
Dependent
No
Yes 2.5 (1.78-3.63)*** 1.6 (1.04-2.44)*
Family type
Living alone
Nuclear 0.3 (0.13-0.52)*** 0.4 (0.18-0.97)*
Extended 0.4 (0.25-0.76)** 0.5 (0.27-0.97)*
Three generation 0.4 (0.20-0.77)** 0.4 (0.21-0.94)*
Skip generation 0.4 (0.17-0.94)* 0.6 (0.22-1.45)
Alcohol, smoking and smokeless tobacco use
Non-user (Never use)
Ex-user 1.4 (1.01-1.95)* 1.3 (0.89-1.88)
Occasional user 1.6 (0.88-2.89) 1.6 (0.81-3.30)
Daily user 1.1 (0.27-4.02) 1.2 (0.25-5.47)
Self-rated health
Very poor/poor/fair
Good/very good 0.7 (0.53-1.03) 0.7 (0.44-0.99)*
Comorbidity
No. diseases
At least one disease 1.0 (0.65-1.49) 0.8 (0.49-1.34)
Two or more diseases 0.9 (0.61-1.45) 0.9 (0.50-1.57)
Low physical activities
No
Yes 0.9 (0.66-1.29) 1.3 (0.88-1.90)
Vision status
Good
Fair/poor 1.2 (0.85-1.58) 0.8 (0.58-1.21)
BMI §
Underweight
Normal 0.7 (0.45-0.95)* 0.9 (0.60-1.41)
Overweigh 0.8 (0.51-1.35) 1.4 (0.77-2.24)
Obese 0.4 (0.26-0.70)* 0.8 (0.44-1.40)
Hypertension
No
Yes 0.7 (0.54-1.02) 0.9 (0.58-1.29)
Diabetes mellitus
No
Yes 0.8 (0.55-1.21) 0.9 (0.57-1.41)
*p<0.05, **p<0.01, ***p<0.001; §BMI: Underweight (11.9-18.4), Normal (18.5-22.9),
Overweigh (23.0-24.9), and Obese (≥25). † Adjusted for age, gender, marital status,
education, dependent, family type, alcohol, smoking and smokeless tobacco use, self-rated
health, no. of comorbidity, low physical activities, vision status, BMI, hypertension,
and diabetes mellitus.”
However, our team are more interested to see how educational background of elderly
effect the cognitive impairment especially among Myanmar elderly those who live in
rural areas. Based on our findings, the policy makers can consider an appropriate
intervention program for illiterate elderly population in rural areas those who are
not getting much attention. More importantly, this study is the very first study reporting
rate of cognitive impairments and its risks factors from Myanmar, a developing country
with limited health resources. The previous studies also reported that low educational
level in earlier life [16,17] was one of the important risk factors for cognitive
decline among elderly. Regard to measurements, we carefully construct the survey questionnaire
and chose variables based on literature review and reflecting the current situation
rural elderly and applicable to the local community setting. To make it clear, we
newly added independent variables categorization in the methods section as follow:
“Independent variables
Socio-demographic characteristics, substance use behaviors, and health problems
were considered as independent variables. The current age was categorized into three
groups (60-69, 70-79, and ≥80) based on the 10-year age intervals. Marital status
was categorized into three groups (single, married, and separated/divorced/windowed).
Education was divided into three groups according to the educational background of
respondents of the elderly: middle school and above, primary school, and only read
and write, and illiterate. Family type was categorized into five groups: living alone,
nuclear, extended, three generations, and skip generation to learn how family structures
affect the cognitive functions of the elderly.
Substance use behaviors were grouped into the following categories: non-users (never
use), ex-users, occasional users, and daily users to see the effect on the cognitive
functions of respondents. Self-rated health, physical activities, and vision status
were divided into two categories. The nutritional status of the elderly may play an
important role in impairment of cognitive function. Therefore, Body mass index (BMI)
was categorized as underweight, normal, overweight, and obese. Hypertension and diabetes
mellitus were grouped into two categories according to self-reported and measurement
results. The measurement cutoff point of blood pressure was 140/90 mmHg (hypertension:
≥140/90 mmHg) and random blood sugar was 200 mg/dL (diabetes mellitus: ≥ 200 mg/dL).”
[Methods, Line 114-127, Page 7-8]
Q-8: Please report the manuscript in accordance with the STROBE guidelines for reporting
observational research.
A-8: Authors’ response: Thank you for your comment. We reported the STROBE guideline
for reporting observation research as follow:
STROBE Statement—Checklist of items that should be included in reports of cross-sectional
studies
Item no. Recommendation Page no. Line no.
Title and abstract 1 (a) Indicate the study’s design with a commonly used term in
the title or the abstract 1
2 1-3
26-50
(b) Provide in the abstract an informative and balanced summary of what was done
and what was found 2 26-50
Introduction
Background/rationale 2 Explain the scientific background and rationale for the investigation
being reported 3-5 63-113
Objectives 3 State specific objectives, including any prespecified hypotheses 5-6
114-127
Methods
Study design 4 Present key elements of study design early in the paper 6 131
Setting 5 Describe the setting, locations, and relevant dates, including periods of
recruitment, exposure, follow-up, and data collection 6-7 131-155
Participants 6 (a) Give the eligibility criteria, and the sources and methods of selection
of participants 6 135-138
Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders,
and effect modifiers. Give diagnostic criteria, if applicable 7-8 156-186
Data sources/ measurement 8* For each variable of interest, give sources of data
and details of methods of assessment (measurement). Describe comparability of assessment
methods if there is more than one group 6
7
145-151
158-168
Bias 9 Describe any efforts to address potential sources of bias 21 394-408
Study size 10 Explain how the study size was arrived at 7 151-155
Quantitative variables 11 Explain how quantitative variables were handled in the analyses.
If applicable, describe which groupings were chosen and why 7-8 170- 168
Statistical methods 12 (a) Describe all statistical methods, including those used
to control for confounding 8 190-193
(b) Describe any methods used to examine subgroups and interactions - -
(c) Explain how missing data were addressed 7 153-155
(d) If applicable, describe analytical methods taking account of sampling strategy
- -
(e) Describe any sensitivity analyses - -
Results
Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers
potentially eligible, examined for eligibility, confirmed eligible, included in the
study, completing follow-up, and analysed 7 150-155
(b) Give reasons for non-participation at each stage 7 152-153
(c) Consider use of a flow diagram - -
Descriptive data 14* (a) Give characteristics of study participants (eg demographic,
clinical, social) and information on exposures and potential confounders 9-12 209-239
Table 1 Table 2
Table 3
(b) Indicate number of participants with missing data for each variable of interest
7 153-155
Outcome data 15* Report numbers of outcome events or summary measures 12 233-239
Table 3
Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted
estimates and their precision (eg, 95% confidence interval). Make clear which confounders
were adjusted for and why they were included 14-16 248-275
Table 4
(b) Report category boundaries when continuous variables were categorized 7 158-168
(c) If relevant, consider translating estimates of relative risk into absolute risk
for a meaningful time period - -
Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions,
and sensitivity analyses
Discussion
Key results 18 Summarise key results with reference to study objectives 16 278-286
Limitations 19 Discuss limitations of the study, taking into account sources of potential
bias or imprecision. Discuss both direction and magnitude of any potential bias 21
394-408
Interpretation 20 Give a cautious overall interpretation of results considering objectives,
limitations, multiplicity of analyses, results from similar studies, and other relevant
evidence 17-21 287-408
Generalisability 21 Discuss the generalisability (external validity) of the study
results 21 404-406
Other information
Funding 22 Give the source of funding and the role of the funders for the present
study and, if applicable, for the original study on which the present article is based
We included funding information in the editorial system.
Q-9: Abstract methods: you conducted a ‘multivariable’, not ‘multivariate’, logistic
regression analysis.
A-9: Authors’ response: Thank you for your question. We corrected the error as you
commented. To reflect the reviewer’s comment,, we revised the previous sentence from
Abstract, “Descriptive statistics were prepared and multivariate logistic regression
analysis performed.” to read as follows: “Descriptive statistics and multivariable
logistic regression analyses were performed.” [Abstract, Line 34-35, Page 2]
Newly added references
22. Conti AA, McLean L, Tolomeo S, Steele JD, Baldacchino A. Chronic tobacco smoking
and neuropsychological impairments: a systematic review and meta-analysis. Neurosci
Biobehav Rev. 2019; 96:143‐154. pmid:30502351
23. Evert, D.L., and Oscar-Berman, M. Alcohol-related cognitive impairments: An overview
of how alcoholism may affect the workings of the brain. Alcohol Health Res World.
19(2):89-96, 1995.
24. Favieri F, Forte G, Casagrande M. The Executive Functions in Overweight and Obesity:
a Systematic Review of Neuropsychological Cross-Sectional and Longitudinal Studies.
Front Psychol. 2019; 10:2126. pmid: 31616340
25. Dye L, Boyle NB, Champ C, Lawton C. The relationship between obesity and cognitive
health and decline. Proc Nutr Soc. 2017; 76(4):443‐454. pmid:28889822
26. Uhlmann RF, Larson EB, Koepsell TD, Rees TS, Duckert LG. Visual impairment and
cognitive dysfunction in Alzheimer’s Disease. J Gen Intern Med. 1991;6(2):126-132.
https://doi.org/10.1007/bf02598307.
29. Cuevas HE. Type 2 diabetes and cognitive dysfunction in minorities: a review of
the literature. Ethn Health. 2019; 24(5):512‐526. pmid:28658961
34. Peters R. Ageing and the brain. Postgrad Med J. 2006; 82(964):84-8. pmid: 16461469
35. Forte G, De Pascalis V, Favieri F, Casagrande M. Effects of Blood Pressure on
Cognitive Performance: a Systematic Review. J Clin Med. 2019; 9(1):34. pmid: 31877865
41. Saw YM, Than TM, Win EM, et al. Myanmar language version of the Revised Hasegawa’s
Dementia Scale. Nagoya J Med Sci. 2018; 80(4):435-450. pmid:30587859
Additional revision
1. To reflect the reviewers’ comments, we revised the references by adding 9 new references
and removing 6 old references. The total number of the references in the revised manuscript
becomes 44. [References: Page 23-27]
2. We revised the abstract as follows: “Abstract
Background: Globally, elderly population with impaired cognitive function, such as
dementia, has been accelerating, and Myanmar is no exception. However, cognitive function
among elderly in Myanmar has rarely been assessed. This study aimed to identify the
rate of cognitive impairment and its risk factors among the elderly in Myanmar.
Methods: This cross-sectional study was conducted at rural health centers in Nay Pyi
Taw Union Territory, Myanmar, from December 2018 to January 2019. In total, 757 elderly
individuals aged 60 years or over (males: 246 [32.5%], females: 511 [67.5%]) were
interviewed using a face-to-face method with a pre-tested questionnaire Descriptive
statistics and multivariable logistic regression analyses were performed.
Results: The rate of impaired cognitive function among participants was 29.9% (males:
23.6%; females: 32.9%). The following participants were more likely to present cognitive
impairment: those aged 70–79 years (adjusted odds ratio [AOR] = 1.8; 95% confidence
interval [CI]: 1.19–2.70) and 80 years or older (AOR = 3.9; 95% CI: 2.25–6.76); those
who were illiterate (AOR = 9.1; 95% CI: 3.82–21.51); and those dependent on family
members (AOR = 1.6; 95% CI: 1.04–2.44). The elderly livening with their families and
those who reported having good health (AOR = 0.7; 95% CI: 0.44–0.99) were less likely
to have cognitive impairment.
Conclusion: Using the HDS-R Myanmar version, this study reported that there out of
five elderly participants had cognitive impairment, and its risk factors, altering
policy makers that Myanmar needs to prepare for adequate healthcare services and social
support for elderly with cognitive impairment. Future research should be performed
not only to detect general cognitive impairment but also to differentiate specific
cognitive domains impairments among Myanmar elderly. Longitudinal studies are needed
to observe the causal and protective factors associated with cognitive impairments
in Myanmar.” [Abstract, Line 27-50, Page 2]
3. In the fifth paragraph of the Results section describing for Table 4, three-generation
family (UOR = 0.4; 95% CI: 0.17–0.94) was changed into skip-generation family (UOR
= 0.4; 95% CI: 0.17–0.94). [Results, Line 257-258, Page 14]
- Attachments
- Attachment
Submitted filename: Response to Reviewers.docx
http://orcid.org/0000-0002-5038-0043
