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 Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 6  |  Issue : 4  |  Page : 172-179

Underweight and overweight/obesity among middle aged and older adults in India: Prevalence and correlates from a national survey in 2017–2018


1 ASEAN Institute for Health Development, Mahidol University, Salaya, Phutthamonthon, Nakhon Pathom, Thailand; Department of Research Administration and Development, University of Limpopo, Polokwane, South Africa
2 Department of Research Administration and Development, University of Limpopo, Polokwane, South Africa

Date of Submission23-Mar-2021
Date of Decision10-Nov-2021
Date of Acceptance20-Nov-2021
Date of Web Publication31-Dec-2021

Correspondence Address:
Prof. Karl Peltzer
Department of Research Administration and Development, University of Limpopo, Polokwane, Private Bag X1106, Sovenga 0727
South Africa
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jncd.jncd_9_21

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  Abstract 


Background and Objective : This study aimed to estimate the prevalence and correlates of underweight and overweight/obesity among middle aged and older adults in India.
Materials and Methods : The cross-sectional sample consisted of 72,262 individuals (≥45 years) from the Longitudinal Aging Study in India Wave 1 in 2017–2018. Multinomial logistic regression was used to estimate the factors associated with underweight and overweight/obesity relative to normal weight.
Results : The prevalence of normal weight (18.5–22.9 kg/m2) was 36.7%, underweight (<18.5 kg/m2) 20.8%, overweight (23.0–24.9 kg/m2) 14.2%, Class I obesity (25.0–29.9 kg/m2) 20.8%, and Class II obesity (≥30.0 kg/m2) 7.4%. In adjusted multinomial logistic regression, the factors positively associated with underweight were older age (≥70 years) (adjusted relative risk ratio [ARRR]: 1.94, confidence interval [CI]: 1.75–2.14), food insecurity (ARRR: 1.19, CI: 1.07–1.33), poor or fair self-rated health status (ARRR: 1.14, CI: 1.05–1.33), and current tobacco use (ARRR: 1.42, CI: 1.31–1.53). The factors negatively associated with underweight were higher education (≥10 years) (ARRR: 0.67, CI: 0.48–0.92), high subjective socioeconomic status (ARRR: 0.78, CI: 0.67–0.92), urban residence (ARRR: 0.72, CI: 0.61–0.84), high life satisfaction (ARRR: 0.83, CI: 0.75–0.91), hypertension (ARRR: 0.64, CI: 0.58–0.69), diabetes (ARRR: 0.50, CI: 0.42–0.59), and heart disease or stroke (ARRR: 0.74, CI: 0.61–0.89). The factors positively associated with overweight/obesity were higher education (≥10 years) (ARRR: 2.09, CI: 1.87–2.33), high subjective socioeconomic status (ARRR: 1.44, CI: 1.31–1.59), urban residence (ARRR: 1.94, CI: 1.79–2.11), high life satisfaction (ARRR: 1.12, CI: 1.04–1.20), hypertension (ARRR: 1.89, CI: 1.76–2.02), type 2 diabetes (ARRR: 1.80, CI: 1.59–2.04), and raised cholesterol (ARRR: 2.75, CI: 2.11–3.58). The factors negatively associated with overweight/obesity were older age (≥70 years) (ARRR: 0.44, CI: 0.39–0.49), male sex (ARRR: 0.59, CI: 0.54–0.64), food insecurity (ARRR: 0.85, CI: 0.76–0.94), vigorous physical activity (>once/week) (ARRR: 0.91, CI: 0.84–0.99), current tobacco use (ARRR: 0.69, CI: 0.64–0.74), and heavy episodic alcohol use (ARRR: 0.70, CI: 0.58–0.85).
Conclusion : One in five middle-aged and older adults in India were underweight and more than two in five were overweight/obese, confirming a dual burden of malnutrition in India.

Keywords: Body weight, India, older adults


How to cite this article:
Pengpid S, Peltzer K. Underweight and overweight/obesity among middle aged and older adults in India: Prevalence and correlates from a national survey in 2017–2018. Int J Non-Commun Dis 2021;6:172-9

How to cite this URL:
Pengpid S, Peltzer K. Underweight and overweight/obesity among middle aged and older adults in India: Prevalence and correlates from a national survey in 2017–2018. Int J Non-Commun Dis [serial online] 2021 [cited 2022 Nov 30];6:172-9. Available from: https://www.ijncd.org/text.asp?2021/6/4/172/334621




  Introduction Top


Both undernutrition and overnutrition have been recognized as major public health problems, in particular in low- to middle-income countries.[1] Worldwide, among adults, the prevalence of undernutrition (<18.5 kg/m2) was 8.8% among men and 9.7% among women, and the prevalence of obesity (BMI ≥30 kg/m2) was 10.8% among men and 14.9% among women.[2] In South Asia, among women (>15 years), the pooled prevalence of underweight was 28% and overweight 17%.[1] Underweight and obesity among older adult people significantly increase morbidity and mortality.[3],[4]

In a national study among older adults (≥50 years) in India in 2007, the prevalence of underweight (<18.5 kg/m2) was 35% and the prevalence of obesity (≥25.0 kg/m2) was 14%.[5] In comparison, in Mexico in 2014–2015, among older adults (≥50 years), the prevalence of underweight was 0.8% and overweight/obesity (≥25.0 kg/m2) was 77.2%,[6] in South Africa in 2010 among older adults (≥50 years), the prevalence of underweight was 8.4%, and overweight/obesity (≥25.0 kg/m2) was 66.8%,[7] and in China in 2017 (≥65 years) the prevalence of underweight was 15.8% and overweight/obesity (≥25.0 kg/m2) was 31.7%.[8] There is a lack of more recent national data on the prevalence and correlates of underweight and overweight among middle and older adults in India.

As reviewed,[9] sociodemographic factors associated with adult underweight may include female sex, younger and older age, lower socioeconomic status, and residing in rural areas. Health variables associated with adult underweight may include poor diets, smoking, and not having chronic conditions. As reviewed[9] sociodemographic factors associated with overweight/obesity include female sex, increasing age, higher socioeconomic status, and urban residence, and health variables associated with overweight/obesity may include, poor diet, physical inactivity, not smoking, diabetes, dyslipidemia, and hypertension. This study aimed to assess the prevalence and correlates of prevalence and correlates of underweight and overweight/obesity among middle-aged persons in India in 2017–2018.


  Materials and Methods Top


Design and participants

This secondary data analysis utilized the data from the cross sectional and nationally representative Longitudinal Aging Study in India (LASI) Wave 1, 2017–2018; “the overall household response rate is 96%, and the overall individual response rate is 87%.”[10] In a household survey, “interview, physical measurement and biomarker data were collected from individuals aged 45 and above and their spouses, regardless of age.”[10] Specific details on the sampling approach are found elsewhere.[10] The study was approved by the “Indian Council of Medical Research Ethics Committee and written informed consent was obtained from the participants.”[10]

Measures

Outcome measure

Anthropometry

Height and weight of adults were measured using the Seca 803 digital scale.”[10] “Body Mass Index = BMI was calculated according to the Asian criteria: underweight (<18.5 kg/m2), normal weight (18.5–22.9 kg/m2), overweight (23.0–24.9 kg/m2), class I obesity (25.0–29.9 kg/m2), and class II obesity (≥30.0 kg/m2).”[11]

Exposure variables

Sociodemographic variables consisted of education (none, ≥1–9 years, ≥10 years), age group (45–69, 60–69, and 70 or more years), sex (male and female), and residence (rural, urban). Subjective socioeconomic status was assessed with the question, “Please imagine a ten-step ladder, where at the bottom are the people who are the worst off – who have the least money, least education, and the worst jobs or no jobs, and at the top of the ladder are the people who are the best off – those who have the most money, most education, and best jobs. Please indicate the number[1],[2],[3],[4],[5],[6],[7],[8],[9],[10] on the rung on the ladder where you would place yourself.”[10] Steps 1 to 3 on the socioeconomic ladder were defined as low, 4–5 as medium, and 6–10 as high socioeconomic status.

Food insecurity was sourced from four items, (1) “In the last 12 months, did you ever reduce the size of your meals or skip meals because there was not enough food at your household? (Yes/No) (2) In the last 12 months, were you hungry but didn't eat because there was not enough food at your household? (Yes/No) (3) In the past 12 months, did you ever not eat for a whole day because there was not enough food at your household? (4) Do you think that you have lost weight in the last 12 months because there was not enough food in your household?”[10] Any positive response to the four questions was scored as one.

Self-rated health status was sourced from the question,In general, would you say your health is excellent, very good, good, fair, or poor?”[10]

Chronic conditions were sourced from the question, “Has any health professional ever told you that you have…?” (1) Diabetes or high blood sugar; (2) chronic heart disease; (3) Stroke; and (4) High cholesterol (Yes/No).”[10] Hypertension or raised blood pressure (BP) was defined as “systolic BP ≥140 mm Hg and/or diastolic BP ≥90 mm Hg (based on the last two averaged of three readings) or where the participant is currently on antihypertensive medication.”[12]

Insomnia symptoms were assessed with four questions: (1) “How often do you have trouble falling asleep?” (2) “How often do you have trouble with waking up during the night?” (3) “How often do you have trouble with waking up too early and not being able to fall asleep again?” (4) “How often do you feel really rested when you wake up in the morning?” Responses options were “never, rarely (1–2 nights per week), occasionally (3–4 nights per week), and frequently (5 or more nights per week).”[10] Insomnia symptoms were coded as “frequently” for the first three symptoms and “never or rarely” for the fourth symptom as one. Participants who reported any of these four symptoms were classified as having sleep problems.[13]

Major depressive disorder (MDD) in the past 12 months was assessed with the Health and Retirement Study Composite International Diagnostic Interview short form[14]), using criteria of the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association.[15] Study respondents were required to “endorse either anhedonia or depressed mood for most of the day for most of a 2-week period or more,” and those who fulfilled this criterion “completed an additional seven symptoms: lost interest, feeling tired, change in weight, trouble with sleep, trouble concentrating, feeling down, and thoughts of death.”[16] “Those with a score ≥3 were considered to meet the criteria for having MDD in the previous 12 months; MDD symptomology scores ranged from 0 to 7.”[16]

Life satisfaction was measured with the 5-item Satisfaction With Life Scale (SWLS).[17] Total scores ranged from 5–35, with 25–35 indicating high life satisfaction.[17] Cronbach's alpha for the SWLS in this study was 0.86.

Vigorous physical activity

“For vigorous activity, respondents were asked about their involvement in running or jogging, swimming, going to a health center/gym, cycling, digging with a spade or shovel, heavy lifting, chopping, farm work, fast bicycling, and cycling with loads.”[10] Responses were trichotomized into 1 = hardly ever/never, 2 = less than twice a week, and 3 = more than once a week.[18]

Current tobacco use was assessed from (1) “Do you currently smoke any tobacco products (cigarettes, bidis, cigars, hookah, cheroot, etc.)? and (2) Do you use smokeless tobacco (such as chewing tobacco, gutka, pan masala, etc.)?”[10]

Heavy episodic alcohol use was assessed with the question, “In the last 3 months, how frequently on average, have you had at least 5 or more drinks on one occasion?”[10] and defined as “one to three days per month, 1 to 4 days per week, five or more days per week, or daily.”

Data analysis

Descriptive statistics were applied to describe health status, sociodemographic information, and functional disability. Unadjusted and adjusted logistic regression was utilized to assess the predictors of ADL and IADL difficulty, separately. P < 0.05 was accepted as significant, missing values were excluded, and no multi-collinearity was found. Statistical analyses were conducted using STATA software version 15.0 (Stata Corporation, College Station, TX, USA), taking the complex study design into account.


  Results Top


Participant characteristics

The sample consisted of 72,226 middle- and older aged individuals from India. [Table 1] shows the detailed participant characteristics. The prevalence of normal weight (18.5–22.9 kg/m2) was 36.7%, underweight (<18.5 kg/m2) 20.8%, overweight (23.0–24.9 kg/m2) 14.2%, class I obesity (25.0–29.9 kg/m2) 20.8%, and class II obesity (≥30.0 kg/m2) 7.4% [Table 1].
Table 1: Sample and nutritional status among older adults in India, 2017–2018

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Associations with underweight and overweight/obesity

In adjusted multinomial logistic regression, factors positively associated with underweight were older age (≥70 years) (adjusted relative risk ratio [ARRR]: 1.94, confidence interval [CI]: 1.75–2.14), food insecurity (ARRR: 1.19, CI: 1.07–1.33), poor or fair self-rated health status (ARRR: 1.14, CI: 1.05–1.33), and current tobacco use (ARRR: 1.42, CI: 1.31–1.53). Factors negatively associated with underweight were higher education (≥10 years) (ARRR: 0.67, CI: 0.48–0.92), high subjective socioeconomic status (ARRR: 0.78, CI: 0.67–0.92), urban residence (ARRR: 0.72, CI: 0.61–0.84), high life satisfaction (ARRR: 0.83, CI: 0.75–0.91), hypertension (ARRR: 0.64, CI: 0.58–0.69), diabetes (ARRR: 0.50, CI: 0.42–0.59), and heart disease or stroke (ARRR: 0.74, CI: 0.61–0.89).

The factors positively associated with overweight/obesity were higher education (≥10 years) (ARRR: 2.09, CI: 1.87–2.33), high subjective socioeconomic status (ARRR: 1.44, CI: 1.31–1.59), urban residence (ARRR: 1.94, CI: 1.79–2.11), high life satisfaction (ARRR: 1.12, CI: 1.04–1.20), hypertension (ARRR: 1.89, CI: 1.76–2.02), type 2 diabetes (ARRR: 1.80, CI: 1.59–2.04), and raised cholesterol (ARRR: 2.75, CI: 2.11–3.58). The factors negatively associated with overweight/obesity were older age (≥70 years) (ARRR: 0.44, CI: 0.39–0.49), male sex (ARRR: 0.59, CI: 0.54–0.64), food insecurity (ARRR: 0.85, CI: 0.76–0.94), vigorous physical activity (>once/week) (ARRR: 0.91, CI: 0.84–0.99), current tobacco use (ARRR: 0.69, CI: 0.64–0.74), and heavy episodic alcohol use (ARRR: 0.70, CI: 0.58–0.85) [Table 2].
Table 2: Multivariable associations with underweight and overweight/obesity (with normal weight as reference category)

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  Discussion Top


In this national 2017–2018 India LASI survey (≥45 years), the prevalence of underweight (20.8%) was lower than in a national study in India in 2007 (35%, ≥50 years),[5] but higher than in China (15.8%, ≥65 years),[8] South Africa (8.4%, ≥50 years),[7] and Mexico (0.8%, ≥50 years).[6] The found prevalence of overweight/obesity (28.2%, ≥25.0 kg/m2) in this study is higher than the prevalence rates found in 2007 in India (14%, ≥25.0 kg/m2, ≥50 years),[5] but lower than in China (31.7%, ≥25.0 kg/m2, ≥65 years),[8] in South Africa (66.8%, ≥25.0 kg/m2, ≥50 years vs. 42.4%, ≥23.0 kg/m2 in this study),[7] and Mexico (77.2%, ≥25.0 kg/m2, ≥50 years vs. 42.4%, ≥23.0 kg/m2 in this study).[6]

The findings show the double burden of undernutrition (20.8%) and overnutrition (42.4%, ≥23 kg/m2) in the lower middle-income country, India. The co-existence of undernutrition (15.6%) and overnutrition (18.0%) has also been found in low-income countries in the Asia Pacific region.[19] The trend in the reduction of underweight and increase of overweight/obesity[2],[19],[20] seems to be confirmed in this study in India. “Rapid dietary and lifestyle transition, it is the leading direction of dual burden with overnutrition increase and diet-related NCDs.“[20],[21] In addition, it is possible that the high prevalence of undernutrition in children under the age of five in India[22] has led to increased overnutrition in adulthood.[23] Increased efforts on policy initiatives and lifestyle changes are needed in India to combat the double malnutrition burden.

The prevalence of underweight increased with age, being 31.6% among those 70 years and older, which was also found in previous studies,[24],[25],[26] and may be attributed to food insecurity.[24],[27] Some previous research showed an association between lower socioeconomic status,[9],[28],[29],[30] lower education,[9],[10],[29] food insecurity, rural residence[20],[29] and underweight, which was also found in this study. Limitation in food intake, high physical labor, and tobacco use may result in a negative energy intake.[31],[32] Poor self-rated health was associated with underweight in this study, as found in a previous study among older adults in India.[30] In line with previous research,[9],[20] hypertension, diabetes and heart disease or stroke reduced the odds of underweight.

Obesity was higher in women (32.9%) compared to men (21.6%), which is in line with previous studies.[20],[25] Consistent with a previous study in India[28] and Mexico,[6] overweight/obesity decreased from middle-to old-age. In line with previous research, residing in urban areas,[5],[6],[26],[29] and higher socioeconomic status,[5],[6],[28],[29] increased the risk of overweight/obesity in this study. The link between urban residence and overweight/obesity may be explained by increased obesogenic factors, such as higher wealth, higher education, and sedentary lifestyles in urban areas.[5]

In agreement with previous studies,[33],[34] this study showed that high physical activity was protective against overweight/obesity. Consistent with some previous research,[9],[20] this study showed a negative association between current tobacco use and the prevalence of overweight/obesity, which may be attributed to the appetite-suppressant effect of nicotine or caffeine.[35]

The study found a positive association between high life satisfaction and overweight/obesity, which compares with studies showing a positive relationship between happiness and overweight/obesity.[20],[36] However, unlike some previous research,[20],[36],[37] we did not find any significant relationship between insomnia symptoms,[36] depression,[37] and overweight obesity. It is possible that in middle- and older aged Indians overweight or obesity is perceived as signifying higher economic status and higher life satisfaction. However, in a study among adult women in India, “three out of four overweight women (BMI between 25 and 29.9 kg/m2) were not happy with their body image, compared to four out of five obese women (BMI of 30 kg/m2 or greater), and almost all (95%) morbidly obese women (BMI of 35 kg/m[2] or greater).”[38]

As shown previously,[20],[39],[40],[41] we found an association between hypertension, diabetes, and raised cholesterol and overweight/obesity, confirming the positive relationship between cardiometabolic comorbidities and overweight/obesity.[40],[41] This result emphasizes the fact that middle- and old-age Indian suffer from several NCD risk factors at the same time,[42] calling for multiple risk factor interventions.[43] Implementing preventive interventions, such as programs improving a healthy diet, appropriate food policies, promotion of physical activity and interrupting sedentary behavior, and community awareness campaigns may help in ameliorating the high burden of overweight and obesity.

Study limitations

The study was limited by its cross-sectional design and the self-report of most data collected. Some variables, such as dietary behavior, were not assessed and should be included in future studies.


  Conclusions Top


One in five middle-aged and older adults in India were underweight and more than two in five were overweight/obese, confirming a dual burden of malnutrition in India. Sociodemographic factors (older age, no education, food insecurity, rural residence, and low socioeconomic status), and health status factors (poor or fair self-rated health status, low life satisfaction, and tobacco use) were identified for underweight. Younger age, female sex, higher education, higher socioeconomic status, urban residence, high life satisfaction, hypertension, diabetes, and raised cholesterol were identified for overweight/obesity, which can be utilized in targeting interventions.

Acknowledgments

“The Longitudinal Aging Study in India Project is funded by the Ministry of Health and Family Welfare, Government of India, the National Institute on Aging (R01 AG042778, R01 AG030153), and United Nations Population Fund, India.”

Authors' contributions

“All authors fulfil the criteria for authorship. SP and KP conceived and designed the research, performed statistical analysis, drafted the manuscript, and made critical revisions of the manuscript for key intellectual content. All authors read and approved the final version of the manuscript and have agreed to the authorship and order of authorship for this manuscript.”

Availability of data and materials

“The data are available at the Gateway to Global Aging Data (www. g2aging. org).”

Ethical approval

The study was approved by the “Indian Council of Medical Research Ethics Committee and written informed consent was obtained from the participants.”[10]

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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