2.1 Setting and participants
This is a secondary analysis of two national studies: The Health, Well-Being, and Aging (SABE) Colombia study and the Korean Longitudinal Study of Aging (KLoSA). The studies were designed to determine the factors that characterize aging in these countries.
In both studies, face-to-face interviews were conducted, and subjects were given sets of questionnaires, concerning sociodemographic characteristics, health-related issues, lifestyle habits, and cognitive function. Participants with incomplete data or those who refused follow-up could not be included in the analysis.
SABE was performed in 2015 with a representative sample of community-dwelling Colombian older adults (age ≥ 60 years). As of 2020, this is the largest database available regarding Latin American older adults. A set of questionnaires on different topics (socio-demographic characteristics, health-related issues, access to health services, cognitive performance, functional status, and financial resources) was applied to all the participants by interviewers at the older adult's household. A total of 3,694 older adults were surveyed for an effective national response rate of 66%. Complete methodology, processes, and objectives are available elsewhere [14].
KLoSA started in 2006, with follow-up every 2 years. A stratified multistage probability sampling was used to obtain a representative sample. The analysis was made on wave 2016. The recruitment of participants and methods used in KLoSA has been described in detail elsewhere [15,16].
2.2 Variables
2.2.1 Body mass index
In Colombia, BMI was determined by anthropometrical data. Bodyweight and height were measured with the patient wearing light indoor clothing, using a Kendall graduated platform scale and a SECA 213® stadiometer (Hamburg, Germany), and BMI was calculated using the formula BMI = weight (kg)/height (m2). Four weight status categories were determined: < 18.5 underweight, 18.5 – 24.9 normal, 25 – 29.9 overweight, > 30 obese[17,18]. In Korea, BMI was measured using self-reports of height and weight (unit: kg/m2). It was based on the Asia-Pacific BMI classification and four categories were determined: underweight (BMI <18.5kg/m2), healthy weight (18.5 BMI <23.0kg/m2), overweight (23.0 BMI <25.0 kg/m2), and obese (BMI >25.0kg/m2)[17].
2.2.2 Basic Activities of Daily Living:
The basic activities of daily living (BADL) refer to self-care and mobility, and their deterioration is closely related to clinical complications, geriatric syndromes, frailty, and dependency [19].
In SABE, BADLs were assessed using the Barthel scale (0-100) [20]. The KLoSA adapted existing BADL and IADL instruments to assess the functional status of the community-dwelling adult population. The KLoSA consists of 7 BADLs items, including dressing, washing the face, bathing, eating, getting out of bed, toileting, and bladder/bowel management. All BADLs variables from KloSA were dichotomized (not need any help = 0 and any kind of help =1) and summed[21,22].
2.2.3 Instrumental Activities of Daily Living:
The instrumental activities of daily living(IADLs) refer to the individual's ability to carry out actions that link the person to the environment, allowing the use of community resources to supply their own needs.
In SABE, IADLs were assessed through the question: “Can you perform the following activities?``: 1) Able to manage own finances, 2) Capable of making daily purchases (especially food) 3) Able to prepare food, 4) Able to manage own medications, 5) Use of public transportation or taxi, 6) Telephone use. Answer options were codified as binary: 0. capable of performing the tasks alone (including both those who perform the activities alone without no difficulties and those who perform the activities alone with difficulty); and 1. not able to do it alone (including both those who perform the activities with any kind of help and those who cannot perform the activity).
In KloSA 10 IADLs items were evaluated, including grooming, housekeeping, preparing meals, laundering, going out, using public transportation, shopping, money management, phone use, and medication management. IADLs were further codified as 0 if the individual was able to perform the task alone (either with or without difficulty) and as 1 if he/ she was not able to perform the task by her/himself [22].
A summary score was created, ranging between 0 and 6 for SABE and 0 to 10 for KLosa; a higher score reflected a greater impairment in IADLs. For the IADL analysis, those persons with any problems in BADLs were excluded, this to assess only those that have problems in IADL and has not progressed to having limitations on BADL.
2.2.4 Cognitive function:
In SABE the Mini-Mental State Examination test ( MMSE ) in its validated Spanish version was used to determine the cognitive status (score ranging from 0-30)[23].
KLoSA subjects were screened using the Korean Mini-Mental State Examination (K-MMSE). The K-MMSE is a validated measure with a score ranging from 0-30[24].
For both populations, the MMSE was dichotomized as normal (> 24) and cognitive impairment (< 24).
2.2.5 Confounding variables
We included sociodemographic factors (age, sex, and years of schooling) and chronic diseases (hypertension, diabetes, COPD, stroke, myocardial infarction, arthritis, and cancer). To further capture the burden of chronic disease, a summary score was created, summing up each disease. Evidence suggests that multi-morbidity accounts more efficiently for the impact on global health in older adults than individual entities [25].
2.3 Statistical analysis
The descriptive analyses were performed by estimating percentages for categorical variables, and means and standard deviations for quantitative variables, and groups were compared using the Pearson chi-square test and t-student test, respectively. For BADL and IADL scores, we fitted a zero-inflated negative binomial regression model to evaluate the differences between nutritional levels, due to the high frequencies of zeros in the datasets (i.e. subjects without decline on BADLs or IADLs). We inverted the Barthel test score in the SABE dataset to allow the correct estimation of the model. Moreover, we fitted a logistic model for the MMSE score dichotomizing its values to normality (> 24) and cognitive decline (<= 24). Based on the literature review, we adjusted the models for the following covariates: sex, level of education, number of comorbidities, and age. We considered significance at P < .05 to evaluate the variables in the model. R software was used to perform all statistical analyses.