Ethics and permission for data collection
Ethics approval for this study [Project Number: PS/2018/1/11] was granted by the University Research Center, Shahjalal University of Science & Technology, Bangladesh and formal permission for data collection was sought from Sylhet Civil Surgeon Office, Bangladesh.
Study design
A cross-sectional study design, involving a person-centered general health assessment and a self-administered survey, was employed in this research.
Setting and participants
This study was conducted from January 2018 to December 2019 in north-eastern region [i.e. Sylhet City Corporation] of Bangladesh. The older adults were approached through Sylhet City Corporation if they were: (i) aged 55 years and older; (ii) living in Sylhet City Corporation; and (iii) agreed to participate in a person-centered general health assessment and an interview. We used the single-stage cluster sampling, and this resulted in a random sample size of 400 participants from 13 administrative wards of the City Corporation.
Data collection
A multi-indicator survey design was employed to explore diverse health issues and conditions of the participants. In accordance with the standards of Helsinki Declaration of 2000 [revised version], the written informed consent was obtained from each of the partisans. After formal consent, the health profile of each participant was collected, through a general health assessment and a structured questionnaire, including self-reported health problems, biomarkers, performance of daily activities and socio-demographic information. Necessary medical equipment were provided to a trained public health assistant to assess and collect anthropometric data.
Outcome measures
General health assessment of height, weight, BMI, BP and RBS
The following Table 1 presents the measurement system of basic health indicators.
Table 1. Measurements of Height, Weight, BMI, BP and RBS
Health Indicators
|
Instruments
|
Measurement procedures
|
Height (m)
|
Height measuring scale (Stadiometer)
|
- Stand without show and simple summer cloths
- Look straight ahead and keep shoulders to level
|
Weight (Kg)
|
Weight measuring scale (Seca Digital)
|
-Keeping normal Summer cloths
- Keeping the respondents simple as far as possible during the measurement
|
BMI (Kg/m2)
|
Computer
|
BP (mmHg.)
|
Electronic BP Monitor (OMB)
Model: BP-1307
|
- Well seated
- After 5 minutes rest
- Average of three consecutive readings
|
RBS (mmol/L)
|
Digital RBS Machine (VivacheckTM Ino.), Model: VGM01
|
- Time between breakfast and lunch
- Time between lunch and dinner
|
Frailty Index (FI30) measurement and coding of variables
Selecting appropriate list of variables is important to compute frailty, while there is no uniformed list and researchers used different variables in calculating Frailty Index. Having a consultation with clinicians, we decided to include 30 categorical variables, specific to the older adults’ frailty, from Tilburg Frailty Indicator [39] (Table 2). Categorical variables were coded using the convention that '0' indicated the absence of the deficit, and '1' the presence of a deficit [10, 11]. For some categorical variables (e.g. self-rated health, BMI etc.) that comprised one or more intermediate responses (e.g. 'average' or 'frequently'), we considered the additional value of ‘0.25’, '0.5' and ‘0.75’. We used short form of Geriatric Depression Scale and a list of 15 questions in a separate questionnaire to collect data [12]. The mathematical formula used for calculating FI30 in this study was as follows:
There was no missing value in the datasets and the calculated FI30 was a continuous score. Obviously, FI30 lies between 0 and 1, however, it was categorized into: FI ≤ 0.10 [No-Frail/Good Health]; 0.10 < FI ≤ 0.21 [Least Frail/Slightly Poor Health], 0.21 < FI ≤ 0.45 [Moderately Frail/Poor Health]; and FI > 0.45 [Frailest/Very Poor Health] for analysis purpose [13]. We further classified the FI30 into No-Frail [Fairly Healthy] (FI ≤ 0.21) and Frail [Medical Conditions] (FI > 0.21) to perform the binary logistic regression model.
Table 2. List of variables included in FI30
SN
|
Variable
|
Code (cut point)
|
SN
|
Variable
|
Code (cut point)
|
1.
|
Self-rated health status
|
[Good=0, Average=0.5, Poor Health=1]
|
16.
|
Do you need help to carry more than 5 KG?
|
[No=0, Yes=1]
|
2.
|
Have any difficulties with hearing
|
[No=0, Yes=1]
|
17.
|
RBS
|
[0 (less than 7.9),
0.5 (7.9-14.9), 1 (15.0 or more)
|
3.
|
Do you have cataracts?
|
[No=0, Yes=1]
|
18.
|
Need any assistance when dressing?
|
[No=0, Yes=1]
|
4.
|
Have any difficulties with vision
|
[No=0, Yes=1]
|
19.
|
Do you need any stick for walking?
|
[No=0, Yes=1]
|
5.
|
Do you have anemia?
|
[No=0, Yes=1]
|
20.
|
Geriatric Depression Scale (GDS)
|
[Normal depression=0, Moderate depression=0.5, Severe depression =1]
|
6.
|
Do you have angina?
|
[No=0, Yes=1]
|
21.
|
Have you had heart attack?
|
[No=0, Yes=1]
|
7.
|
Do you have asthma?
|
[No=0, Yes=1]
|
22.
|
HBP
|
[Normal=0, Mild=0.33, Moderate=0.66, Severe=1]
|
8.
|
Balance (Do you need assistance when standing for 10 sec. with one foot behind the other)
|
[No=0, Yes=1]
|
23.
|
Have you heart murmur?
|
[No=0, Yes=1]
|
9.
|
Bathing (Do you need assistance when bathing?)
|
[No=0, Yes=1]
|
24.
|
Do you have heart problem?
|
[No=0, Yes=1]
|
10.
|
BMI
|
[Normal=0, Moderate=0.50, Severe=1]
|
25.
|
Do you have kidney diseases?
|
[No=0, Yes=1]
|
11.
|
Do you have bronchitis?
|
[No=0, Yes=1]
|
26.
|
Do you have liver diseases?
|
[No=0, Yes=1]
|
12.
|
Stand-ups from chair without using arms
|
[No=0, Yes=1]
|
27.
|
Do you have osteoporosis problem?
|
[No=0, Yes=1]
|
13.
|
Do you have arthritis?
|
[No=0, Yes=1]
|
28.
|
Have you had a Seizure?
|
[No=0, Yes=1]
|
14.
|
Do you need help to others when using toilet?
|
[No=0, Yes=1]
|
29.
|
Have you had a Stroke?
|
[No=0, Yes=1]
|
15.
|
Do you feel lonely?
|
[No=0, Yes=1]
|
30.
|
Have you urinary infection?
|
[No=0, Yes=1]
|
Data analysis
We employed frequency distribution to know the demographic nature of the participants. The relationships between the participants’ physio-psychosocial health and socio-demographic variables were tested using bivariate analysis. The chi-square test was performed to observe the significant association between FI30 and physio-psychosocial variables. A binary logistic regression model was used to identify the risk factors of frailty, where FI30 was a binary dependent variable and physio-psychosocial variables were independent variables. This project’s data management and statistical analyses were carried out through IBM SPSS Statistics 20.0.
To ensure reliability and validity of the study results, we used a number of techniques: (i) employ SPSS 20.0 in data analysis; (ii) explore descriptive statistics of the sample (socio-demographic items) and their means and standard deviations; (iii) reliability analyses of the data used in calculating FI30 (Cronbach’s alpha, the reliability coefficient values for the variables used in FI30 is found 0.70); and (iv) use of two statistical techniques, namely chi-square test and logistic regression [14].