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| ORIGINAL ARTICLE |
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| Year : 2022 | Volume
: 5
| Issue : 4 | Page : 147-153 |
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Nutrition status of lower-income older adults in Thailand during COVID-19 pandemic
Paolo Miguel Manalang Vicerra1, Jose Carlo G. De Pano2, Juniesy Martinez Estanislao3
1 Asian Demographic Research Institute, Shanghai University, Shanghai, China
2 Department of Speech Communication and Theatre Arts, University of the Philippines, Quezon City, Philippines
3 Asian Center, University of the Philippines, Quezon City, Philippines
| Date of Submission | 10-Aug-2022 |
| Date of Decision | 05-Oct-2022 |
| Date of Acceptance | 17-Oct-2022 |
| Date of Web Publication | 22-Nov-2022 |
Correspondence Address:
Juniesy Martinez Estanislao
Asian Center, University of the Philippines, Quezon City
Philippines
 Source of Support: None, Conflict of Interest: None  | 2 |
DOI: 10.4103/shb.shb_150_22
Introduction: The nutrition status of older adults during the COVID-19 pandemic is an area of concern. Lower-income older population of Thailand in particular has been affected with regard to their employment, income, and health status. This study focused on the prevalence of nutrition statuses using body mass index (BMI) of this age group and their association with sociodemographic, health behavior, social connectedness, and economic change factors during the pandemic. Methods: Using the 2021 Survey on Housing and Support Services for Poor Older Adults, a sample of lower-income individuals aged 55 years and over was collected from the five regions of Thailand. The data were analyzed using multinomial logistic regression where being underweight and overweight were compared with normal weight as the reference. Relative risk ratios (RRR) were presented. Results: Living in regions other than Bangkok was found to be associated with a higher risk of underweight status and lower risk of being overweight. Having primary level (RRR = 0.600, P < 0.05) and above primary level of education (RRR = 0.952, P < 0.05) significantly related with lower risk of low BMI. Income inadequacy during the outbreak was found to be positively associated with both underweight (RRR = 1.514, P < 0.05) and overweight (RRR = 1.145, P < 0.05) statuses. Conclusion: The results show the need to understand the dynamics of social backgrounds, such as poverty experience, in order to address the needs and issues of vulnerable older people, particularly during pandemic times.
Keywords: COVID-19, poverty, social determinants of health
How to cite this article:
Vicerra PM, Pano JC, Estanislao JM. Nutrition status of lower-income older adults in Thailand during COVID-19 pandemic. Asian J Soc Health Behav 2022;5:147-53 |
| Introduction | |  |
The COVID-19 pandemic had a massive impact across countries. In Thailand, managing the transmission of the disease started on March 2020 when the first lockdown measures were implemented. Until the present, various developments had occurred relating to the peaks and troughs in the number of positive cases vis-à -vis the policies on how to prevent further adverse outcomes.[1] What had persisted throughout the outbreak of the disease was the implementation of sheltering in place in different areas of the country whenever infection rates were becoming severe. It has been noted in the literature regarding vulnerable populations such as older adults in Thai society that psychological distress, among other things, had been negatively affected due to the general situation.[2],[3] What is lacking in the literature thus far is the nutritional status of older people.
Learning from previous pandemics, the resilience of a population can be strengthened by developing the efficacy of public health approaches.[4] A major component of public health response is going beyond clinical interventions. In various countries, certain literature has identified that the physical and psychological health of older people has associations with undesirable behaviors and outcomes related to the COVID-19 pandemic.[5],[6],[7],[8] Mental health was among such factors, which included fear and stress of the prevailing disease. A longitudinal study found that fear of COVID-19 was observed to affect the performance of preventable behaviors in Taiwan.[6] Similar observations were found in certain locations in Iran.[5] An aspect that may be considered context-specific is resource deprivation. Mental stress during the pandemic was also seen to affect general well-being among older people, especially those of lower-income backgrounds.[7]
The stated observation pertaining to economic resources is key to achieving subsistence as an important part of resilience during emergency situations. Nutrition management from the global to the individual level has been underscored to better respond during the COVID-19 pandemic.[9] Macro-level issues such as food pricing and transport will trickle down to the level of households where the issue of food availability and consumption can further affect the nutritional status of individuals.
Focusing on nutrition status is important in the context of COVID-19 as different outcomes are brought about by health statuses of individuals. Malnutrition, whether in the form of being energy deficient or being overweight, can result in more severe outcomes when infected with the novel coronavirus disease.[10],[11] Older adults are particularly vulnerable with regard to malnutrition risks. The effects of sheltering in place for this age group are not only limited to social isolation and loneliness but also the access to proper diet because food availability was hampered.[9],[12]
Nutrition and weight have been an issue in Thailand in recent years. Being overweight, even being obese, have been particularly highlighted because of increasing prevalence such that between 1991 and 2009, the obesity rate had almost doubled for adults.[13] The rate for men by 2009 was around 28% and 40% among women according to the 2008–2009 National Health Examination Survey.[13] Undernutrition and being underweight have also been noted among older people in selected communities.[14],[15] Socioeconomic statuses were observed to be associated with being overweight and underweight in different circumstances.[15],[16]
There is a dearth of studies concerning the nutrition status of lower-income older persons in Thailand. The COVID-19 context has made the issue of health, social, and economic vulnerabilities among older Thai adults more apparent.[2] About half of the older population in the country continues to engage in gainful activities as most are not enrolled in any pension program.[17] The precarious nature of this age group's economic situation can influence nutrition status as dietary choices have been noted to be different in certain societies whereby they can limit food intake altogether[10] or consume more snacks with higher calories and even alcohol.[18] The focus of this study then is on the correlates of health, social connection, and economic factors brought about by the pandemic situation with the nutrition status of lower-income older individuals.
| Methods | | |
Data and sample
The Survey on Housing and Support Services for Poor Older Adults was used for this study.[19] Individuals aged 55 years old and above across the five regions of the country were the sample. These older adults were selected after being identified as those earning <40,000 Baht (around US$ 1330) annually, or those who were beneficiaries of a cash transfer program called “Card for the Poor.” The goal of the survey was to gather information on the general living condition of later-life adults but COVID-19 information was integrated into the survey since data collection occurred during the easing of lockdown measures in the country from May to June 2021. A multistage cluster and stratification sampling design were used for the survey. The total sample of the survey was 2139 individuals.
In this study, those who had a proxy answer a portion or the entire survey were excluded. The analytic sample was 2025 respondents. To check the representativeness of the analytic sample, a comparison was done with the unrestricted sample. The difference between the samples was not statistically significant in reference to key variables specifically age, gender, residence, and marital status.
Measures
Body mass index
The body mass index (BMI) was utilized as the anthropometric indicator of nutrition status. Height and weight information were self-identified by the respondents. From the responses, the BMI was calculated and the World Health Organization[20] standard classification was referred as its application to Thai population offered modest estimates.[21] The respective BMI for the categories were: underweight was <18.5 kg/m2; normal weight was 18.5 kg/m2 to 22.9 kg/m2; and overweight was at least 23 kg/m2.
Sociodemographic covariates
The age was categorized into groups, namely 55–74 years, 65–74, and 75 years and over. Gender, region of residence (Bangkok, Central, North, Northeast, and South), urban–rural residence, marital status (never married, formerly married, and currently married), and education attainment (lower than primary, Primary level [4th to 6th grade levels], and higher than primary level).
Health behavior and status
Smoking tobacco and drinking alcohol were measured as dichotomous variables of whether the respondent engaged in those behaviors. As a general indicator of health in the duration of the pandemic, self-rated health (SRH) was asked of the respondents where a 5-point scale of very good to very bad was the choices. A dichotomous variable was generated where the outcome category was “Poor SRH” where the choices of fair, bad, and very bad were combined. Finally, the health status prospect information was collected by asking respondents if they were worried that their health status will worsen if the COVID-19 pandemic persists.
Social connectedness
The knowledge and affect toward being connected within a social network were identified as important to older person's nutrition needs,[22] especially during emergencies as the COVID-19 pandemic.[23] Items pertaining to this are in the survey whereby respondents were asked if they can contact family members, neighbors, and community leads which include village health volunteers and the community head. Each item was dichotomized and the outcome category was the lack of contact with the said actors.
Economic status and changes
Survey participants were asked if their level of income changed from the prepandemic period. It was measured here by showing those whose incomes became lower over the duration of the pandemic. Respondents were also enquired of the adequacy of their income. The categories used for the present analysis was between those with adequate and inadequate income.
In the survey, individuals were asked if they experienced skipping meals at any point in the past year from the time of the survey enumeration. Food insecurity was measured to reflect those who did skip meals. Finally, housing worries and concerns were in the survey. This is also an apt measure of economic status that has associations with health outcomes during the COVID-19 pandemic.[24] Housing worries was an index created from four issues on the respondents' dwellings: (1) too small for the residents, (2) far from groceries/shops, (3) distant from any medical facility, and (4) unsafe environment. From the said index, a dichotomous variable was generated where the outcome category was having at least one issue or worry.
Ethical consideration
The ethics approval was provided by a committee of the Institute for the Development of Human Resource Protection (COA-IHRP2020117).
Statistical analysis
Descriptive statistics on frequency of nutrition status was done by sociodemographic, health-related, and economic status factors. Multinomial logistic regression was applied on the data to estimate the association of the covariates with being underweight and overweight such that the reference category was normal weight. There was no issue with multicollinearity which was tested by estimating the variance inflation factor and the respective tolerance of the independent variables.[25]
| Results | | |
The prevalence of respective nutrition status by sociodemographic covariates are shown in [Table 1]. Individuals aged at least 75 years old have the highest prevalence of being underweight compared to the other age groups. The said age group also has the lowest prevalence with regard to being overweight. Women also have a higher prevalence of being overweight at around 38% compared to men at about 28%. | Table 1: Percentage distribution of sociodemographic characteristics by body mass index category
Click here to view |
Differences between regions were stark whereby about 44% of the sample residing in Bangkok were overweight compared to the other regions which have 34% and less in prevalence. The Northeast region on the other hand has the highest prevalence of lower-income people being underweight at about 13%. Living in rural areas, being never married, and having lower primary education attainment also have higher prevalence of being underweight in their respective characteristic measures.
The prevalence of nutrition statuses by health and economic factors is shown in [Table 2]. Having consumed alcohol and smoked tobacco have higher proportions of being underweight at 10% and 12%, respectively. The proportion of underweight individuals is similar based on SRH but, for those with poor SRH had a higher prevalence of being overweight. Other notable characteristics that have higher prevalence of being underweight were lack of connection within the neighborhood at around 11% and also having inadequate income at 10%. | Table 2: Distribution of health and economic characteristics by nutrition status
Click here to view |
The relative risk ratios based on a multinomial logistic regression model are shown in [Table 3]. People in the oldest age group were observed to be 1.4 times more likely to be underweight than have normal weight. All the regions have also been found to have greater risks of being underweight compared to Bangkok. Having smoked tobacco, lacking in connectedness with neighbors, and earning inadequate income were the other factors associated with being underweight. Higher education attainment levels were the only factors negatively associated with the outcome whereby finishing primary level and those with above primary level were 0.6 and 0.9 times less likely to be underweight, respectively. | Table 3: Multinomial logistic regression model of nutrition status and the covariates with normal weight as the reference category
Click here to view |
The covariates associated with being overweight relative to those who have normal weight were different. Individuals in the older ages were negatively associated with being overweight. Living in regions other than Bangkok has also been found to be negatively associated with higher BMI. Having poor SRH was about 1.3 times likely to be overweight while having inadequate income was shown to be 1.1 times likely to be in this BMI category.
| Discussion | | |
This study examined the prevalence of underweight, overweight, and normal weight among older Thai adults. A unique context was highlighted whereby the data utilized were collected after about a year since the pandemic was declared and transmission control measures were implemented. Although the COVID-19 situation affected and will continue to affect all people, differences in impact that manifest are different among individuals depending on their social and economic backgrounds.
Age was observed here as having association with nutrition status. Similar to Bangladesh adults, being in older age groups was related with underweight status while younger adults ages had associations with being overweight.[26] A study in a province in Thailand had also noted the underweight status of people in advanced ages and surmised that it may be linked with the oral health, i.e., chewing ability, whereby individuals had decreased capacity for proper ingestion.[15] This confluence with the general physical health and nutrition status can be provided with greater attention among community-dwelling older adults.
A sociodemographic factor was observed to be associated with either malnutrition status– regional residence. Compared to Bangkok, all the regions were associated with underweight and overweight statuses albeit in different direction of relationships. The differentiation of Bangkok from other regions of the country has been stark in terms of a range of aspects from vitamin deficiency[27] to overall mortality level.[28] Access to less favorable options as fast-food meals and having less opportunity for physical activities given the built environment have been identified to influence this particular nutrition status outcome.[29] During the pandemic, the said aspect of lifestyle has been further underscored among older persons in different regions.[1]
A difference in factor association with nutrition status was observed here. Lack of social connected was positively related with underweight status. This has been observed in the literature as older individuals may bear loneliness and affect their food intake.[22] Furthermore, it was cautioned that, during the pandemic, sheltering in place may prevent older people from accessing appropriate food supplies.[23] With regard to overweight status, it was observed in the present study to be associated with poor SRH during COVID-19. Being overweight was an identified risk factor for the novel coronavirus disease.[11] As people were aware of this risk, the prevention of movement from their homes also limited their opportunity for physical activity which can lead to people with higher BMI to feel vulnerable.
Socioeconomic challenges were highlighted in the present study such that the sample consisted of lower-income older people but furthermore, the perception of negative changes to their income level and its inadequacy were explored. It was observed that higher education attainment levels were related to lower underweight prevalence. Complementary to education, income inadequacy during the pandemic was associated with being underweight. Poverty has been observed in the literature as having effects on the process of aging.[30] The earlier aging of people refers to the earlier onset of biological risks such as negative changes to the BMI.
Poverty influences the general lifestyle of individuals. It was mentioned previously that income adequacy was related with being underweight. Some people feel restrained regarding the precarious state of their finances which results to their limiting of food intake.[15],[26] A notable observation though in the present study was the positive association of income inadequacy with being overweight as well. This is in lieu though with findings from other developing economies.[29],[31] Having less expenditure capacity affects food choices toward higher calorie diet but with less nutritional value. The differences in circumstance of becoming underweight or overweight stem from choices based on limited economic resource within the context of COVID-19.
The present study found social and economic factors that affect the health of lower-income older adults. Limitations need to be acknowledged. First, the nature of the survey utilized was cross-sectional and therefore there is a lack of causation in the results. Second, all health-related variables, particularly on weight and height, were self-reported. Finally, this study utilized BMI as the only measure for nutrition status. BMI was also defined as being the same for all adults regardless of being in the younger or advanced age category even if they have different health baselines, i.e., the threshold for over-nutrition for older adults is higher.[32]
| Conclusion | | |
This study theme is important to assess the social situation of vulnerable populations during emergency situations. In the context of Thailand with its low-and middle-income country-status (LMIC-status) and having an ageing population, the nutrition status of older individuals is important to assess as it is related to other adverse health risks as hypertension and diabetes among others. Being malnourished also has implications to the wellbeing of individuals. The COVID-19 pandemic is an unprecedented event whereby the various implications to people of different health and socioeconomic backgrounds need to be rapidly evaluated to better deliver the needs of the population and become better prepared.
Acknowledgments
The access to survey data used here was possible through Dr. Thananon Buathong.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3]
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