Skip to main content
PMC home page
Scientific Reports logoLink to Scientific Reports
. 2023 Jan 20;13:1136. doi: 10.1038/s41598-023-28397-3

Food insecurity and health outcomes among community-dwelling middle-aged and older adults in India

Supa Pengpid 1,2,3, Karl Peltzer 1,4,5,✉
PMCID: PMC9859825  PMID: 36670204

Abstract

The study assessed associations between food insecurity and mental, physical, and behavioural health outcomes in India. The study analysed national cross-sectional population-based data (N = 72,262;  ≥ 45 years) from in India in 2017–2018. The overall prevalence of food insecurity was 9.7%. Food insecurity was significantly positively associated with poor mental health [low life satisfaction (AOR: 2.75, 95% CI 2.35–3.23), low self-reported health (AOR: 1.61, 95% CI 1.11–1.42), insomnia symptoms (AOR: 1.64, 95% CI 1.45–1.85), depressive symptoms (AOR: 2.21, 95% CI 1.97–2.48), major depressive disorder (AOR: 2.37, 95% CI 2.03–2.77), Alzheimer’s/dementia (AOR: 1.75, 95% CI 1.13–2.69), and poorer cognitive functioning (AOR: 0.68, 95% CI 0.49–0.93)], poor physical health [bone or joint disease (AOR: 1.18, 95% CI 1.04–1.34), angina (AOR: 1.80, 95% CI 1.58–2.06), underweight (AOR: 1.28, 95% CI 1.16–1.40), chronic lung disease (AOR: 1.22, 95% CI 1.03–1.45), and functional disability (AOR: 1.68, 95% CI 1.47–1.92)], and health risk behaviour [tobacco use (AOR: 1.13, 95% CI 1.01–1.25), heavy episodic drinking (AOR: 1.45, 95% CI 1.10–1.91) and physical inactivity (AOR: 1.42, 95% CI 1.21–1.67)]. Furthermore, food insecurity was negatively associated with overweight/obesity (AOR: 0.80, 95% CI 0.73–0.88). Food insecurity was associated with seven poor mental health indicators, five poor physical health conditions, and three health risk behaviours. Programmes and policies that improve food availability may help improve mental and physical health among middle-aged and older adults in India.

Subject terms: Ecology, Risk factors

Introduction

Globally, 25.9% of the population experienced hunger or had inadequate access to sufficient and nutritious food in 20191. In India, for example, the prevalence of food insecurity was 8.5% in Delhi and Chennai2, in rural Puducherry, India, 31.7% had food insecurity3, and in a national sample of persons 50 years and older in India, 10.2% reported moderate food insecurity and 7.3% severe food insecurity4. Despite economic development, India has high food insecurity, hunger, malnutrition, undernourishment (16.3%) and underweight in children (34.4%)5–7. In India, the most populous nation to come, the population of older persons is globally growing faster than the general population to reach 34% of the total population by the end of the century8. Along with food insecurity various adverse health outcomes, including chronic diseases, poor mental health, disability, and reduced quality of life may be associated with ageing in India8.

Several reviews and large and small studies showed an association between food insecurity and poor mental health, such as depression9–13, mental morbidity14, anxiety9, not anxiety10, sleep disorder9,10, poorer subjective well-being15, and poorer cognitive function11,16,17. In terms of physical health outcomes, food insecurity has been associated with cardiometabolic risks, such as excess weight18, underweight4, hypertension18,19, self-reported hypertension20, cardiovascular diseases (angina, coronary heart disease, and heart attack)19, dyslipidaemias18, diabetes18,21,22, and physical frailty23.

Moreover, food insecurity has been associated with other physical chronic conditions, such as chronic lung disease21, inflammatory diseases or joint/muscular pain and functional limitations24. Regarding health risk behaviours, food insecurity and tobacco use were found to be bidirectionally associated25, while other studies found an association between food insecurity and physical inactivity and smoking26, heavy alcohol use, and smoking27.

However, studies investigation the association between food insecurity and poor mental health, poor physical conditions and health risk behaviours are usually conducted with one or two negative health outcomes and in high-income countries. To address, this shortfall, we aim to investigate the associations between food insecurity and a wide variety of health outcomes, including seven mental health, 11 physical conditions and three health risk behaviours, in a low-resourced country, India. To gain an understanding on associations between food insecurity with a wide range of health outcomes may assist in managing clients with food insecurity problems. Therefore, this study aimed to assess the associations between food insecurity and 21 health indicators in middle-aged and older adults in a national community-based study in India.

Methods

Study population and procedures

In a national cross-sectional household survey in India in 2017–2018, 72,262 individuals (≥ 45 years) and their spouses, regardless of age (response rate 87%) responded to a structured interview and physical measurements29. “Detailed information on sampling methods and sample size are published in the Longitudinal Ageing Study in India (LASI) Report28. The effective sample size for the present study was 72,262 middle-aged and older adults, including 41,685 males and 30,577 females. On the exposure variable food insecurity, 849 (1.2%) individuals were missing, which were excluded in the multivariable analysis, making the sample size 72,413. On the social and demographic variables (or control variables) the range of missing cases was from 0% for age, sex, and residence status to 1.0% for health insurance. The proportion of missing cases for the health outcome variables ranged from 0.3% for diagnosed hypertension and diabetes to 10.6% for body mass index. We compared missing with non-missing health outcome variables on social and demographic factors, and we could largely not find significant differences, except for older age and social participation with BMI and cognition, and no schooling, low organized religiosity, and rural residence with cognition. The study was approved by the “Indian Council of Medical Research (ICMR) Ethics Committee and written/oral informed consent was obtained from the participants”.

Measures

Health outcome variables

Self-rated health status was defined as “1 = poor or fair and 0 = excellent, very good, or good”28; coded as in previous surveys in India29. Self‐rated health has been found to have high predictive validity for mortality30.

Life satisfaction was measured with the Health and Retirement Study item, “Please, think about your life as a whole. How satisfied are you with it?” “completely satisfied, very satisfied, somewhat satisfied (coded as 0), not very satisfied, or not at all satisfied (coded as 1)?”31,32.

Cognitive functioning was measured with four components (orientation, immediate and delayed word recall, and serial 7 s)33. These cognitive functioning tests have been previously validated in the Indian population34–37.

Insomnia symptoms (any of 4 items) were assessed with the “Jenkins Sleep Scale (JSS-4)”38–40 (Cronbach α 0.86).

Depressive symptoms (scores four or more) were obtained from the “Center for Epidemiological Studies Depression Scale (CES-D-10)”41,42. (Cronbach α was 0.79). The CES-D has been validated in the Indian population42, and in the Indian older adult population43.

Major depressive disorder (score ≥ 3, 0–7) was assessed with the “Composite International Diagnostic Interview Short Form (CIDI-SF)”44,45. CIDI-SF has been validated for use in general population health surveys46.

Anthropometry: “Height and weight of adults were measured using the Seca 803 digital scale.”28. The “body mass index = BMI was calculated according to Asian criteria: underweight (< 18.5 kg/m2), and overweight/obesity (≥ 23.0 kg/m2)”47,48.

Health care provider diagnosed “Alzheimer’s/Dementia,” “bone/joint disorder (arthritis/rheumatism, Osteoporosis/other bone/joint diseases),” “chronic heart diseases,” “Hypertension,” “diabetes,” “chronic lung disease” and “stroke” (Yes/No)28.

Angina was measured with the “World Health Organization’s Rose angina questionnaire,” defined on the basis of “discomfort at walking uphill or hurrying, or at an ordinary pace on level ground. Furthermore, the pain should be located at the sternum or in the left chest and arm, causing the patient to stop or slow down, and the pain should resolve within 10 min when the patient stops or slows down”49–51.

Elevated blood pressure (BP) or hypertension: “systolic BP ≥ 140 mm Hg and/or diastolic BP ≥ 90 mm Hg (based on the last two averaged of three readings) or on antihypertensive medication”52.

The difficulties of activities of daily living (ADL) (e.g. “Getting in or out of bed”) and instrumental activities of daily living (IADL) (e.g. “Taking medications”) were evaluated with 6 and 7 items, respectively (Yes, No)53,54; (Cronbach α was 0.89 in this study). Responses were dichotomized into “0 or 1 and ≥ 2 ADL and IADL items”. Both ADL and IADL measures have shown acceptable validity among older adults in India55.

Substance use included current heavy alcohol use (≥ 5 drinks on one occasion) and current tobacco use28.

Physical inactivity was defined as “hardly ever or never engaging in vigorous or moderate physical activity”28.

Exposure variable

LASI utilized four items of an 18-item Household Food Security Scale (HFSS)56,57, similar to items from the 8-item Food Insecurity Experience Scale Survey Module for Individuals (FIES SM-I)58. The HFSS items are validated in the Indian setting59. Both scales are to produce reliable food insecurity prevalence in diverse countries and use different scoring procedures, ranging from summative scores to individual scores13,17. The individual items are:

  • (1) “In the last 12 months, did you 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? (Yes/No)”.

  • (4) “Do you think that you have lost weight in the last 12 months because there was not enough food in your household? (Yes/No)”57.

The Cronbach α for the four-item HFSS was 0.82 in this study. Overall food insecurity was defined as 1–4 positive responses to four items.

Covariates

Covariates consisted of sex, education, age, receipt of health insurance (“Are you covered by health insurance?”), marital and residential status, subjective economic status (1–3 = low, 4–5 = medium, and 6–10 = high, and caste (Scheduled tribes, scheduled castes, other backward classes, and none of these)28. Social participation [e.g. “Eat-out-of-house (restaurant/hotel)”] was defined as at least one of six daily to at least once a month social activity60. Organizational religiosity included frequency of attending religious services (“1 (low) = not at all, 2 (medium) = 1–3 times a month or ≥ 1 times/year, and 3(high) ≥ once a week”28.

Data analysis

Considering the clustered study design, data analyses were performed with “STATA software version 15.0 (Stata Corporation, College Station, TX, USA).” Descriptive statistics were used for all participant characteristics and all outcome variables. Univariable and multivariable logistic and linear regressions were applied to assess the associations between food insecurity and binary and scale health outcomes (dependent variables). Variables significant in univariable analyses were included in the multivariable models. The health outcome variables were selected based on a previous review of the literature9–27. Multivariable models were adjusted for all social and demographic factors, and health variables. P < 0.05 was accepted as significant. The variance inflation factor (VIF) was calculated to check for multicollinearity, and none was found between the study variables.

Ethical review

The study was approved by the “Indian Council of Medical Research (ICMR) Ethics Committee,” and written/oral informed consent was obtained from participants28.

Ethics approval and consent to participate

The study was approved by the “Indian Council of Medical Research (ICMR) Ethics Committee in January 2017 and written or oral informed consent was obtained from the participants.” All methods were carried out in accordance with relevant guidelines and regulations and in accordance with the World Medical Association Declaration of Helsinki.

Results

Participant characteristics

The study analysed national cross-sectional population-based data (N = 72,262;  ≥ 45 years) from in India in 2017–2018. Most (75.1%) belonged to reserved castes or tribes, 49.5% had no formal education, 68.2% lived in rural areas, 20.7% had health insurance, 75.6% were married, 75.1% belonged to reserved castes / tribes, 68.2% lived in rural areas, 74.5% engaged in medium or high organised religiosity, and 54.4% had social participation. The prevalence of seven mental health indicators included, for example, 27.6% depressive symptoms and 0.7% Alzheimer’s/dementia, 11 physical health indicators included, for example, 46.1% overweight/obesity and 1.8% stroke, and three health risk behaviours, for example, 30.4% current tobacco use. The past 12-month prevalence of food insecurity was 9.7% (see Table 1).

Table 1.

Sample and food insecurity characteristics among middle-aged and older adults in India, 2017–2018 (N = 72,262).

Variable Variable specification Sample Food insecurity in the past 12 months
Overall (4 items) Cut size/skip meals Hungry but not eat Not eat for whole day Lost weight due to lack of food
% or M (SD) % % % % %
Social and demographic factors
 All 9.7 6.0 5.3 3.8 4.8
 Age in years 45–59 54.1 9.1 5.6 5.0 3.4 4.1
60 or more 45.9 10.5 6.4 5.6 4.2 5.7
 Sex Female 58.0 9.8 6.0 5.4 3.6 4.6
Male 42.0 9.7 5.9 5.2 4.0 5.2
 Formal education 1 or more years 50.5 7.3 4.2 3.8 2.5 3.4
None 49.5 12.2 7.8 6.8 5.1 6.4
 Socioeconomic status Low 37.2 14.3 10.0 8.7 6.4 7.4
Medium 38.7 8.1 4.3 4.0 2.8 3.6
High 24.1 5.4 2.5 2.2 1.3 2.8
 Health insurance No 79.3 10.2 6.3 5.5 4.1 5.3
Yes 20.7 8.0 4.7 4.8 2.5 3.0
 Marital status Not married 24.4 11.8 7.9 6.6 5.1 6.1
Married 75.6 9.0 5.3 4.9 3.3 4.5
 Religion Hindu 81.9 9.3 5.7 5.0 3.7 4.8
Muslim 11.7 12.7 7.3 7.1 4.6 6.3
Christian 3.0 10.8 8.1 7.8 3.9 2.9
Sikh 1.8 6.6 4.2 3.3 1.6 1.7
Other 1.7 10.6 6.6 7.0 4.7 5.7
 Caste/tribe None of below 24.9 6.9 4.2 3.6 2.4 3.5
Scheduled Caste 19.7 13.3 9.2 7.9 6.4 6.7
Scheduled Tribe 8.8 11.2 7.3 6.0 4.3 5.6
Other Backward Class 46.7 9.4 5.2 5.0 3.3 4.6
 Residence Rural 68.2 11.3 7.2 6.2 4.6 6.0
Urban 31.8 6.3 3.3 3.3 2.0 2.4
 Region North 11.3 5.8 2.9 2.3 1.4 2.9
Northeast 3.6 7.5 4.3 2.5 1.5 3.7
East 23.4 10.9 7.9 6.6 4.5 5.8
Central 20.8 13.7 7.8 6.4 5.6 9.4
West 16.5 8.0 4.9 4.4 3.7 3.1
South 24.4 8.4 5.0 5.6 2.9 2.3
 Organised religiosity Low 25.5 11.1 7.1 6.2 4.7 6.0
Medium 46.9 9.2 5.7 5.1 3.5 4.5
High 27.6 9.3 5.3 4.8 3.5 4.4
 Social participation Yes 54.4 9.7 5.7 5.3 3.5 4.5
Mental health
 Self-reported health (fair or poor) Yes 39.7 13.0 8.3 7.8 5.2 6.8
 Life satisfaction Low 11.1 24.3 17.5 16.3 12.2 12.7
 Cognitive functioning Scores: 0–32; M (SD) 18.7 (5.1) 17.4 (5.1) 16.9 (5.1) 17.0 (5.2) 16.7 (4.8) 17.2 (5.0)
 Insomnia symptoms Yes 12.7 16.4 10.5 9.0 7.0 10.0
 Depressive symptoms Yes 27.6 16.9 11.2 9.9 7.2 9.3
 Major depressive disorder Yes 7.6 23.7 17.6 15.7 12.0 14.6
 Alzheimer’s/dementia Yes 0.7 18.4 12.9 11.8 8.5 10.9
Physical health
 General underweight Yes 28.4 14.0 8.9 8.0 6.2 8.5
 General overweight/obesity Yes 46.1 6.8 4.1 3.4 2.4 2.6
 Bone/joint disease Yes 15.7 11.6 7.6 6.5 5.1 6.3
 Hypertension (measured) Yes 40.4 8.9 5.7 4.8 3.5 4.1
 Hypertension (self-reported) Yes 26.4 10.3 5.9 5.8 3.5 4.8
 Heart disease Yes 8.6 10.3 7.2 5.5 2.9 4.3
 Angina Yes 8.6 16.2 12.9 11.8 8.5 10.9
 Stoke Yes 1.8 15.8 10.4 8.6 4.3 8.7
 Diabetes Yes 11.6 8.2 3.8 4.7 2.4 3.4
 Chronic lung disease Yes 6.3 12.4 7.3 6.9 5.2 6.8
 Functional limitationsa 2 or more 28.8 13.8 9.0 7.9 5.8 7.4
Health risk behaviour
 Current tobacco use Yes 30.4 11.5 7.6 6.6 5.1 6.3
 Heavy alcohol use Yes 2.9 15.5 10.3 10.1 5.0 5.9
 Physical inactivity Yes 23.7 11.8 6.5 5.7 4.2 6.6
Open in a new tab

aDifficulties with two or more Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL).

Associations between food insecurity and health outcome indicators

In the fully adjusted multivariable models, food insecurity was significantly positively associated with poor mental health [low life satisfaction (AOR: 2.75, 95% CI 2.35–3.23), low self-reported health (AOR: 1.61, 95% CI 1.11–1.42), insomnia symptoms (AOR: 1.64, 95% CI 1.45–1.85), depressive symptoms (AOR: 2.21, 95% CI 1.97–2.48), major depressive disorder (AOR: 2.37, 95% CI 2.03–2.77), Alzheimer’s/dementia (AOR: 1.75, 95% CI 1.13–2.69), and poorer cognitive functioning (AOR: 0.68, 95% CI 0.49–0.93)], poor physical health [bone or joint disease (AOR: 1.18, 95% CI 1.04–1.34), angina (AOR: 1.80, 95% CI 1.58–2.06), underweight (AOR: 1.28, 95% CI 1.16–1.40), chronic lung disease (AOR: 1.22, 95% CI 1.03–1.45), and functional disability (AOR: 1.68, 95% CI 1.47–1.92)], and health risk behaviour [tobacco use (AOR: 1.13, 95% CI 1.01–1.25), heavy episodic drinking (AOR: 1.45, 95% CI 1.10–1.91) and physical inactivity (AOR: 1.42, 95% CI 1.21–1.67)]. Furthermore, food insecurity was negatively associated with overweight/obesity (AOR: 0.80, 95% CI 0.73–0.88). Food insecurity was not significantly associated with hypertension (self-reported and measured), heart disease, stroke, and diabetes. Compared to the summative food insecurity measure, similar associations were found when analysing each food insecurity item (cut size/skip meal, hungry but not eat, not eat for the whole day, and lost weight due to lack of food) separately (see Tables 2, 3, 4).

Table 2.

Associations between food insecurity and mental health indicators.

Outcome variables Response format Food insecurity Univariable odds ratio/exp (Coef.) (95% CI) Multivariable odds ratio/exp (Coef.) (95% CI)a
Self-reported health (fair or poor) No Overall food insecurity 1 Reference 1 Reference
Yes 1.82 (1.61 2.05)*** 1.61 (1.11, 1.42)***
Cut size/skip meals 1.95 (1.74, 2.20)*** 1.64 (1.43, 1.87)***
Hungry but not eat 2.19 (1.85, 2.61)*** 1.93 (1.54, 2.41)***
Not eat for whole day 1.90 (1.65, 2.19)*** 1.58 (1.36, 1.84)***
Lost weight due to lack of food 1.97 (1.75, 2.22)*** 1.62 (1.43, 1.83)***
Life satisfaction (low) No Overall food insecurity 1 Reference 1 Reference
Yes 3.75 (3.20, 4.40)*** 2.75 (2.35, 3.23)***
Cut size/skip meals 4.50 (3.83, 5.27)*** 3.00 (2.51, 3.88)***
Hungry but not eat 4.77 (3.83, 5.94)*** 3.36 (2.74, 4.12)***
Not eat for whole day 4.99 (4.14, 6.01)*** 3.20 (2.61, 3.92)***
Lost weight due to lack of food 3.66 (3.10, 4.31)*** 2.49 (2.10, 2.96)***
Cognitive functioning Scale Overall food insecurity 1 Reference 1 Reference
0.24 (0.15, 0.38)*** 0.68 (0.49, 0.93)*
Cut size/skip meals 0.16 (0.11, 0.23)*** 0.54 (0.40, 0.74)***
Hungry but not eat 0.17 (0.08, 0.38)*** 0.52 (0.31, 0.87)*
Not eat for whole day 0.13 (0.09, 0.19)*** 0.52 (0.37, 0.72)***
Lost weight due to lack of food 0.20 (0.14, 0.29)*** 0.76 (0.54, 1.06)
Insomnia symptoms No Overall food insecurity 1 Reference 1 Reference
Yes 2.04 (1.80, 2.32)*** 1.64 (1.45, 1.85)***
Cut size/skip meals 2.08 (1.80, 2.41)*** 1.51 (1.33, 1.74)***
Hungry but not eat 1.98 (1.66, 2.37)*** 1.49 (1.27, 1.73)***
Not eat for whole day 2.21 (1.83, 2.65)*** 1.66 (1.37, 2.00)***
Lost weight due to lack of food 2.60 (2.21, 3.06)*** 1.98 (1.68, 2.35)***
Depressive symptoms No Overall food insecurity 1 Reference 1 Reference
Yes 2.68 (2.32, 3.08)*** 2.21 (1.97, 2.48)***
Cut size/skip meals 3.04 (2.64, 3.50)*** 2.31 (2.02, 2.64)***
Hungry but not eat 2.96 (2.39, 3.67)*** 2.31 (1.96, 2.72)***
Not eat for whole day 3.08 (2.63, 3.61)*** 2.29 (1.93, 2.71)***
Lost weight due to lack of food 3.16 (2.73, 3.66)*** 2.43 (2.08, 2.83)***
Major depressive disorder No Overall food insecurity 1 Reference 1 Reference
Yes 2.90 (2.46, 3.41)*** 2.37 (2.03, 2.77)***
Cut size/skip meals 4.04 (3.34, 4.90)*** 2.70 (2.28, 3.20)***
Hungry but not eat 4.01 (3.15, 5.12)*** 2.77 (2.29, 3.36)***
Not eat for whole day 4.29 (3.54, 5.20)*** 2.94 (2.38, 3.62)***
Lost weight due to lack of food 4.09 (3.40, 4.92)*** 2.70 (2.21, 3.29)***
Alzheimer’s/dementia No Overall food insecurity 1 Reference 1 Reference
Yes 2.10 (1.47, 3.01)*** 1.75 (1.13, 2.69)*
Cut size/skip meals 2.36 (1.54, 3.62)*** 1.99 (1.21, 3.29)**
Hungry but not eat 2.40 (1.55, 3.72)*** 1.98 (1.20, 3.29)**
Not eat for whole day 2.40 (1.47, 3.91)*** 2.24 (1.28, 3.94)**
Lost weight due to lack of food 2.41 (1.58, 3.67)*** 1.89 (1.12, 3.19)*
Open in a new tab

CI  confidence interval.

***p < 0.001, **p < 0.01, *p < 0.05.

aAdjusted for all social and demographic factors, and health variables.

Table 3.

Associations between food insecurity and physical health indicators.

Outcome variables Response format Food insecurity Univariable odds ratio (95% CI) Multivariable odds ratio (95% CI)a
General underweight No Overall food insecurity 1 Reference 1 Reference
Yes 1.78 (1.62, 1.95)*** 1.28 (1.16, 1.40)***
Cut size/skip meals 1.80 (1.61, 2.01)*** 1.25 (1.11, 1.40)***
Hungry but not eat 1.94 (1.71, 2.20)*** 1.36 (1.19, 1.55)***
Not eat for whole day 2.07 (1.81, 2.38)*** 1.41 (1.22, 1.64)***
Lost weight due to lack of food 2.27 (1.99, 2.59)*** 1.51 (1.32, 1.73)***
General overweight/obesity No Overall food insecurity 1 Reference 1 Reference
Yes 0.56 (0.51, 0.62)*** 0.80 (0.73, 0.88)***
Cut size/skip meals 0.54 (0.47, 0.61)*** 0.80 (0.71, 0.91)***
Hungry but not eat 0.53 (0.47, 0.60)*** 0.81 (0.71, 0.92)***
Not eat for whole day 0.51 (0.44, 0.58)*** 0.79 (0.68, 0.92)**
Lost weight due to lack of food 0.38 (0.33, 0.43)*** 0.57 (0.50, 0.65)***
Bone/joint disease No Overall food insecurity 1 Reference 1 Reference
Yes 1.27 (1.12, 1.44)*** 1.18 (1.04, 1.34)*
Cut size/skip meals 1.37 (1.18, 1.60)*** 1.25 (1.07, 1.47)**
Hungry but not eat 1.29 (1.08, 1.55)** 1.16 (0.97, 1.38)
Not eat for whole day 1.47 (1.22, 1.78)*** 1.29 (1.05, 1.58)*
Lost weight due to lack of food 1.40 (1.18, 1.64)*** 1.32 (1.10, 1.58)**
Hypertension (measured) No Overall food insecurity 1 Reference 1 Reference
Yes 0.86 (0.78, 0.95)** 0.93 (0.85, 1.02)
Cut size/skip meals 0.90 (0.80, 1.01) 0.97 (0.87, 1.09)
Hungry but not eat 0.90 (0.79, 1.02) 0.95 (0.84, 1.08)
Not eat for whole day 0.86 (0.75, 0.99)* 0.97 (0.84, 1.12)
Lost weight due to lack of food 0.75 (0.67, 0.86)*** 0.86 (0.76, 0.97)*
Hypertension (self-report) No Overall food insecurity 1 Reference –
Yes 1.09 (0.94, 1.26)
Cut size/skip meals 0.97 (0.84, 1.12)
Hungry but not eat 1.15 (0.89, 1.48)
Not eat for whole day 0.92 (0.78, 1.08)
Lost weight due to lack of food 0.98 (0.86, 1.12)
Heart disease No Overall food insecurity 1 Reference –
Yes 1.07 (0.76, 1.49)
Cut size/skip meals 1.24 (0.79, 1.92)
Hungry but not eat 1.05 (0.62, 1.77)
Not eat for whole day 0.76 (0.48, 1.19)
Lost weight due to lack of food 0.88 (0.61, 1.27)
Angina No Overall food insecurity 1 Reference 1 Reference
Yes 1.93 (1.68, 2.23)*** 1.80 (1.58, 2.06)***
Cut size/skip meals 1.89 (1.59, 2.25)*** 1.69 (1.42, 2.01)***
Hungry but not eat 1.72 (1.40, 2.10)*** 1.58 (1.31, 1.92)***
Not eat for whole day 2.12 (1.76, 2.55)*** 1.93 (1.60, 2.34)***
Lost weight due to lack of food 2.19 (1.85, 2.58)*** 1.96 (1.65, 2.32)***
Stroke No Overall food insecurity 1 Reference 1 Reference
Yes 1.76 (1.15, 2.70)** 1.21 (0.90, 1.62)
Cut size/skip meals 1.85 (1.02, 3.34)* 1.12 (0.79, 1.57)
Hungry but not eat 1.69 (0.83, 3.42) 0.93 (0.62, 1.39)
Not eat for whole day 1.17 (0.80, 1.70) 0.99 (0.64, 1.54)
Lost weight due to lack of food 1.89 (1.39, 2.57)*** 1.44 (0.96, 2.14)
Diabetes No Overall food insecurity 1 Reference 1 Reference
Yes 0.80 (0.56, 1.16) 0.84 (0.72, 1.01)
Cut size/skip meals 0.59 (0.48, 0.73)*** 0.80 (0.65, 0.99)*
Hungry but not eat 0.86 (0.46, 1.62) 0.78 (0.62, 1.02)
Not eat for whole day 0.60 (0.46, 0.77)*** 0.87 (0.67, 1.13)
Lost weight due to lack of food 0.65 (0.53, 0.81)*** 0.95 (0.75, 1.19)
Chronic lung disease No Overall food insecurity 1 Reference 1 Reference
Yes 1.34 (1.10, 1.63)** 1.22 (1.03, 1.45)*
Cut size/skip meals 1.27 (1.03, 1.55)* 1.11 (0.92, 1.34)
Hungry but not eat 1.34 (1.02, 1.75) 1.21 (0.95, 1.54)
Not eat for whole day 1.44 (1.06, 1.95)* 1.24 (0.92, 1.67)
Lost weight due to lack of food 1.46 (1.13, 1.89)** 1.19 (0.92, 1.53)
Functional limitationsb No Overall food insecurity 1 Reference 1 Reference
Yes 1.84 (1.62, 2.08)*** 1.68 (1.47, 1.92)***
Cut size/skip meals 1.96 (1.74, 2.21)*** 1.77 (1.54, 2.03)***
Hungry but not eat 1.92 (1.60, 2.30)*** 1.74 (1.45, 2.09)***
Not eat for whole day 2.06 (1.79, 2.36)*** 1.80 (1.56, 2.09)***
Lost weight due to lack of food 2.02 (1.79, 2.28)*** 1.77 (1.55, 2.03)***
Open in a new tab

CI  confidence interval.

***p < 0.001,**p < 0.01, *p < 0.05.

aAdjusted for all social and demographic factors, and health variables, bDifficulties with two or more Activities of Daily Living (ADL) and/or Instrumental Activities of Daily Living (IADL).

Table 4.

Associations between food insecurity and health risk behaviour.

Outcome variables Response format Food insecurity Univariable odds ratio (95% CI) Multivariable odds ratio (95% CI)a
Current tobacco use No Overall food insecurity 1 Reference 1 Reference
Yes 1.32 (1.19, 1.47)*** 1.13 (1.01, 1.25)*
Cut size/skip meals 1.48 (1.32, 1.66)*** 1.22 (1.07, 1.40)**
Hungry but not eat 1.42 (1.22, 1.65)*** 1.26 (1.09, 1.47)**
Not eat for whole day 1.63 (1.41, 1.69)*** 1.17 (0.97, 1.41)
Lost weight due to lack of food 1.51 (1.34, 1.69)*** 1.09 (0.95, 1.25)
Heavy alcohol use No Overall food insecurity 1 Reference 1 Reference
Yes 1.74 (1.21, 2.51)** 1.45 (1.10, 1.91)**
Cut size/skip meals 1.86 (1.12, 3.07)* 1.33 (0.92, 1.92)
Hungry but not eat 2.07 (1.23, 3.47)** 1.48 (1.01, 2.16)*
Not eat for whole day 1.36 (1.02, 1.82)* 1.20 (0.86, 1.67)
Lost weight due to lack of food 1.23 (0.89, 1.71) 1.27 (0.88, 1.84)
Physical inactivity No Overall food insecurity 1 Reference 1 Reference
Yes 1.33 (1.16, 1.53*** 1.27 (1.12, 1.43)***
Cut size/skip meals 1.12 (0.96, 1.31) 0.99 (0.86, 1.14)
Hungry but not eat 1.09 (0.91, 1.31) 1.04 (0.89, 1.20)
Not eat for whole day 1.18 (0.99, 1.40) 1.05 (0.88, 1.25)
Lost weight due to lack of food 1.58 (1.37, 1.82)*** 1.42 (1.21, 1.67)***
Open in a new tab

CI confidence interval.

***p < 0.001, **p < 0.01, *p < 0.05, aAdjusted for all social and demographic factors, and health variables.

Discussion

The study aimed to assess the associations between food insecurity and mental, physical, and behavioural health outcomes in India. Results show for the first time that food insecurity was significantly positively associated with seven poor mental health indicators (low life satisfaction, low self-reported health, insomnia symptoms, depressive symptoms, major depressive disorder, Alzheimer’s/dementia, and poorer cognitive functioning), five poor physical health conditions (bone/joint disease, angina, underweight, chronic lung disease, and functional disability), and three health risk behaviours (tobacco use, heavy alcohol use and physical inactivity).

Consistent with previous research9–17, we found largely strong associations between food insecurity and poor mental health indicators, the highest for low life satisfaction (2.75), followed by major depressive disorder (2.37), depressive symptoms (2.21), Alzheimer’s or dementia (1.75), insomnia symptoms (1.64), poorer self-reported health (1.61) and lower cognitive functioning (0.68). Possible mechanisms involved in the relationship between food insecurity and poor mental health, including cognitive functioning, include chronic stress triggered by food insecurity that increases the release of glucocorticoids that produce depressive symptoms and cognitive decline11,61. Since this was a cross-sectional study, we were unable to determine the direction of the relationship between food insecurity and poor mental health. Some studies suggest a bidirectional association between food insecurity and poor emotional health62,63. In addition to food insecurity leading to poor mental health, poor mental health may for example reduce income generating activities limiting the ability to purchase food and thus become food insecure63. Nutrition interventions by reducing food insecurity may positively impact on mental health64.

Regarding cardiometabolic risk factors, we found, in agreement with previous research19 that food insecurity was associated with angina and in unadjusted analysis with stroke, but not with overweight or obesity, hypertension, and diabetes, as previously found18,19,21,22. It is possible that food insecure individuals with angina and/or stroke have a reduced ability to handle difficult chronic conditions, which may increase the risk or worsen their cardiovascular disease19. Contrary to previous findings in high-income settings18, we found that food insecurity was protective against overweight or obesity, and, in agreement with a previous study in India4, that food insecurity was associated with underweight. In this study, 28.4% of the participants were underweight, emphasising the need to address undernutrition together with food insecurity. Furthermore, consistent with previous results21,24, this study showed an association between food insecurity and bone or joint diseases, chronic lung diseases, and functional limitations. It is possible that people with food insecurity were more likely to have chronic conditions, such as angina, bone or joint disease, and underweight, which may lead to a greater risk of functional limitation or disability24. Perhaps people with food insecurity eat less healthy foods (such as energy-dense foods) and this has an impact on the presence of chronic diseases65. Indian ageing adults with angina, bone or joint diseases, chronic lung diseases, and functional limitations could benefit from focused interventions addressing food insecurity26. On the other hand, the relationship between food insecurity and cardiovascular disease risk, including angina, may be bidirectional19,66, and having bone or joint diseases and/or functional disability may reduce accessibility to food thereby increasing food insecurity67,68.

Consistent with several previous investigations26,27,69, food insecurity was associated with tobacco use, heavy drinking, and physical inactivity in this study. Some research70 indicates that expenditure on tobacco and/or alcohol may reduce the food budget. The association between food insecurity and physical inactivity may be attributed to food insecurity leading to poor physical and/or mental health, which could result in physical inactivity26. Substance users may need to receive health education that quitting tobacco use and quitting or reducing alcohol use not only has health but also financial benefits and can improve food security, and on a population level food availability and substance use control should be promoted70. Furthermore, some research has shown a bidirectional association between food insecurity and tobacco use25,71, and similarly some cross-sectional studies showed an association between food insecurity and problem drinking72 and other studies found an association between problem drinking and food insecurity in men27, potentially suggesting a bidirectional association between food insecurity and problem drinking.

Considering the possible link between food insecurity and various negative health outcomes, food insecurity issues should be included in health care management, e.g. by including food security screenings as part of health assessment, followed by an appropriate intervention such as food access programmes62. In addition, long-term programmes to prevent food insecurity are needed, such as linking food insecure households to social services17.

Strength and limitations of the study

The study utilized a large nationally representative sample of aging adults in India showing the associations between food insecurity and a wide range of mental, physical, and behavioural health outcomes in a middle-income context. Food insecurity was only assessed with four items and not a full scale, which would have allowed for assessing different degrees of food insecurity. The large sample size may introduce the possibility of type 1 error, and findings should be interpreted with this limitation. Furthermore, most measures were limited due to self-report and the cross-sectional study design hinders us from drawing causative conclusions. Moreover, there is the possibility of reverse causation, considering that various studies25,62,63,71 showed that poor health outcomes may cause food insecurity and food insecurity may cause poor health outcomes. Our cross-sectional data do not allow us to draw causal or directional conclusions, given that we were unable to totally remove the effect of reverse causality. However, we controlled our models for a wide range of covariates and health variables as confounders in the analyses. Longitudinal studies are needed to confirm or disconfirm the direction of our findings.

Conclusions

One in ten individuals (≥ 45 years) in India had past 12-month food insecurity. Food insecurity was significantly positively associated with poor mental health (low life satisfaction, low self-reported health, insomnia symptoms, depressive symptoms, major depressive disorder, Alzheimer’s/dementia, and poorer cognitive functioning), poor physical health (bone or joint disease, angina, underweight, chronic lung disease, and functional disability), and health risk behaviour (tobacco use, heavy episodic drinking, and physical inactivity). Programmes and policies that improve food availability may help improve mental and physical health among middle-aged and older adults in India.

Acknowledgements

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.

Author 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.

Data availability

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

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Food, Agricultural Organisation. The State of Food Security and Nutrition in the World 2019: Transforming Food Systems for Affordable Healthy Diets. Safeguarding against Economic Slowdowns and Downturns (2020). http://www.fao.org/documents/card/en/c/ca9692en (Accessed 12 June 2021).
  • 2.Rautela G, Ali MK, Prabhakaran D, Narayan KMV, Tandon N, Mohan V, Jaacks LM. Prevalence and correlates of household food insecurity in Delhi and Chennai India. Food Secur. 2020;12(2):391–404. doi: 10.1007/s12571-020-01015-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Nagappa B, Rehman T, Marimuthu Y, Priyan S, Sarveswaran G, Kumar SG. Prevalence of food insecurity at household level and its associated factors in rural Puducherry: A cross-sectional study. Indian J. Community Med. 2020;45(3):303–306. doi: 10.4103/ijcm.IJCM_233_19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Schrock JM, McClure HH, Snodgrass JJ, Liebert MA, Charlton KE, Arokiasamy P, Naidoo N, Kowal P. Food insecurity partially mediates associations between social disadvantage and body composition among older adults in india: Results from the study on global AGEing and adult health (SAGE) Am. J. Hum. Biol. 2017 doi: 10.1002/ajhb.23033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Narayanan S. Food security in India: The imperative and its challenges. Asia Pac. Policy Stud. 2015;2:197–209. doi: 10.1002/app5.62. [DOI] [Google Scholar]
  • 6.George NA, McKay FH. The public distribution system and food security in India. Int. J. Environ. Res. Public Health. 2019;16(17):3221. doi: 10.3390/ijerph16173221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Global Food Security Index. India. https://impact.economist.com/sustainability/project/food-security-index/explore-countries/india (Accessed 12 November 2022).
  • 8.United Nations Population Fund 2017. Caring for Our Elders: Early Responses - India Ageing Report—2017. UNFPA, New Delhi, India.
  • 9.Arenas DJ, Thomas A, Wang J, DeLisser HM. A systematic review and meta-analysis of depression, anxiety, and sleep disorders in US adults with food insecurity. J. Gen. Intern. Med. 2019;34(12):2874–2882. doi: 10.1007/s11606-019-05202-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Pourmotabbed A, Moradi S, Babaei A, Ghavami A, Mohammadi H, Jalili C, Symonds ME, Miraghajani M. Food insecurity and mental health: A systematic review and meta-analysis. Public Health Nutr. 2020;23(10):1778–1790. doi: 10.1017/S136898001900435X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.McMichael AJ, McGuinness B, Lee J, Minh HV, Woodside JV, McEvoy CT. Food insecurity and brain health in adults: A systematic review. Crit. Rev. Food Sci. Nutr. 2021;62:1–16. doi: 10.1080/10408398.2021.1932721. [DOI] [PubMed] [Google Scholar]
  • 12.Smith L, Il Shin J, McDermott D, Jacob L, Barnett Y, López-Sánchez GF, Veronese N, Yang L, Soysal P, Oh H, Grabovac I, Koyanagi A. Association between food insecurity and depression among older adults from low- and middle-income countries. Depress Anxiety. 2021;38(4):439–446. doi: 10.1002/da.23147. [DOI] [PubMed] [Google Scholar]
  • 13.Muhammad T, Sulaiman KM, Drishti D, Srivastava S. Food insecurity and associated depression among older adults in India: Evidence from a population-based study. BMJ Open. 2022;12(4):e052718. doi: 10.1136/bmjopen-2021-052718. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Saha SK, Pradhan P, Haldar D, Maji B, Agarwal W, Sarkar GN. Magnitude of mental morbidity and its correlates with special reference to household food insecurity among adult slum dwellers of Bankura, India: A cross-sectional survey. Indian J. Psychol. Med. 2019;41(1):54–60. doi: 10.4103/IJPSYM.IJPSYM_129_18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Frongillo EA, Nguyen HT, Smith MD, Coleman-Jensen A. Food insecurity is associated with subjective well-being among individuals from 138 countries in the 2014 Gallup World Poll. J. Nutr. 2017;147(4):680–687. doi: 10.3945/jn.116.243642. [DOI] [PubMed] [Google Scholar]
  • 16.Na M, Dou N, Ji N, Xie D, Huang J, Tucker KL, Gao X. Food insecurity and cognitive function in middle to older adulthood: A systematic review. Adv. Nutr. 2020;11(3):667–676. doi: 10.1093/advances/nmz122. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Srivastava S, Muhammad T. Rural-urban differences in food insecurity and associated cognitive impairment among older adults: Findings from a nationally representative survey. BMC Geriatr. 2022;22(1):287. doi: 10.1186/s12877-022-02984-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Miguel EDS, Lopes SO, Araújo SP, Priore SE, Alfenas RCG, Hermsdorff HHM. Association between food insecurity and cardiometabolic risk in adults and the elderly: A systematic review. J. Glob. Health. 2020;10(2):020402. doi: 10.7189/jogh.10.020402. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Liu Y, Eicher-Miller HA. Food insecurity and cardiovascular disease risk. Curr. Atheroscler. Rep. 2021;23(6):24. doi: 10.1007/s11883-021-00923-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Beltrán S, Pharel M, Montgomery CT, López-Hinojosa IJ, Arenas DJ, DeLisser HM. Food insecurity and hypertension: A systematic review and meta-analysis. PLoS One. 2020;15(11):e0241628. doi: 10.1371/journal.pone.0241628. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Vaccaro JA, Huffman FG. Sex and race/ethnic disparities in food security and chronic diseases in U.S. older adults. Gerontol. Geriatr. Med. 2017;3:2333721417718344. doi: 10.1177/2333721417718344. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Abdurahman AA, Chaka EE, Nedjat S, Dorosty AR, Majdzadeh R. The association of household food insecurity with the risk of type 2 diabetes mellitus in adults: A systematic review and meta-analysis. Eur. J. Nutr. 2019;58(4):1341–1350. doi: 10.1007/s00394-018-1705-2. [DOI] [PubMed] [Google Scholar]
  • 23.Muhammad T, Saravanakumar P, Sharma A, Srivastava S, Irshad CV. Association of food insecurity with physical frailty among older adults: Study based on LASI, 2017–18. Arch. Gerontol. Geriatr. 2022;103:104762. doi: 10.1016/j.archger.2022.104762. [DOI] [PubMed] [Google Scholar]
  • 24.Venci BJ, Lee SY. Functional limitation and chronic diseases are associated with food insecurity among U.S. adults. Ann. Epidemiol. 2018;28(3):182–188. doi: 10.1016/j.annepidem.2018.01.005. [DOI] [PubMed] [Google Scholar]
  • 25.Kim-Mozeleski JE, Pandey R. The intersection of food insecurity and tobacco use: A scoping review. Health Promot. Pract. 2020;21(1_suppl):124S–138S. doi: 10.1177/1524839919874054. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Mendy VL, Vargas R, Cannon-Smith G, Payton M, Enkhmaa B, Zhang L. Food insecurity and cardiovascular disease risk factors among mississippi adults. Int. J. Environ. Res. Public Health. 2018;15(9):2016. doi: 10.3390/ijerph15092016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Bergmans RS, Coughlin L, Wilson T, Malecki K. Cross-sectional associations of food insecurity with smoking cigarettes and heavy alcohol use in a population-based sample of adults. Drug Alcohol Depend. 2019;205:107646. doi: 10.1016/j.drugalcdep.2019.107646. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.International Institute for Population Sciences (IIPS), NPHCE, MoHFW, Harvard T. H. Chan School of Public Health (HSPH) and the University of Southern California (USC). Longitudinal Ageing Study in India (LASI) Wave 1, 2017–18, India Report, International Institute for Population Sciences, Mumbai, 2020.
  • 29.Srivastava S, Muhammad T, Paul R, Thomas AR. Multivariate decomposition analysis of sex differences in functional difficulty among older adults based on Longitudinal Ageing Study in India, 2017–2018. BMJ Open. 2022;12(4):e054661. doi: 10.1136/bmjopen-2021-054661. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Schnittker J, Bacak V. The increasing predictive validity of self-rated health. PLoS One. 2014;9(1):e84933. doi: 10.1371/journal.pone.0084933. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Cheung F, Lucas RE. Assessing the validity of single-item life satisfaction measures: Results from three large samples. Qual. Life Res. 2014;23(10):2809–2818. doi: 10.1007/s11136-014-0726-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Diener E, Lucas RE, Oishi S. Advances and open questions in the science of subjective well-being. Collabra Psychol. 2018;4(1):15. doi: 10.1525/collabra.115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Lee J, Smith JP. Regional disparities in adult height, educational attainment and gender difference in late- life cognition: Findings from the Longitudinal Aging Study in India (LASI) J. Econ. Ageing. 2014;4:26–34. doi: 10.1016/j.jeoa.2014.02.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Lee J, Shih RA, Feeney KC, Langa KM. Cognitive Health of Older Indians: Individual and Geographic Determinants of Female Disadvantage, WR-889. RAND Corporation; 2011. [Google Scholar]
  • 35.Ganguli M, Ratlciff G, Chandra V, Sharma SD, Gilby S, et al. A Hindi version of the MMSE: The development of a cognitive screening instrument for a largely illiterate rural population in India. Int. Psychogeriatr. 1995;10:367–377. [Google Scholar]
  • 36.Tiwari SC, Tripathi RK, Kumar A. Applicability of the Mini-mental State Examination (MMSE) and the Hindi Mental State Examination (HMSE) to the urban elderly in India: A pilot study. Int. Psychogeriatr. 2009;21(1):123–128. doi: 10.1017/S1041610208007916. [DOI] [PubMed] [Google Scholar]
  • 37.Mathuranath PS, Cherian JP, Mathew R, George A, Alexander A, Sarma SP. Mini mental state examination and the Addenbrooke's cognitive examination: Effect of education and norms for a multicultural population. Neurol. India. 2007;55(2):106–110. doi: 10.4103/0028-3886.32779. [DOI] [PubMed] [Google Scholar]
  • 38.Jenkins CD, Stanton BA, Niemcryk SJ, Rose RM. A scale for the estimation of sleep problems in clinical research. J. Clin. Epidemiol. 1988;41(4):313–321. doi: 10.1016/0895-4356(88)90138-2. [DOI] [PubMed] [Google Scholar]
  • 39.Cho E, Chen TY. The bidirectional relationships between effort-reward imbalance and sleep problems among older workers. Sleep Health. 2020;6(3):299–305. doi: 10.1016/j.sleh.2020.01.008. [DOI] [PubMed] [Google Scholar]
  • 40.Fabbri M, Beracci A, Martoni M, Meneo D, Tonetti L, Natale V. Measuring subjective sleep quality: A review. Int. J. Environ. Res. Public Health. 2021;18(3):1082. doi: 10.3390/ijerph18031082. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Andresen EM, Malmgren JA, Carter WB, Patrick DL. Screening for depression in well older adults: Evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale) Am. J. Prev. Med. 1994;10(2):77–84. doi: 10.1016/S0749-3797(18)30622-6. [DOI] [PubMed] [Google Scholar]
  • 42.Kumar S, Nakulan A, Thoppil SP, Parassery RP, Kunnukattil SS. Screening for depression among community-dwelling elders: Usefulness of the center for epidemiologic studies depression scale. Indian J. Psychol. Med. 2016;38(5):483–485. doi: 10.4103/0253-7176.191380. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Chokkanathan S, Mohanty J. Factor structure of the CES-D scale among older adults in Chennai India. Aging Ment. Health. 2013;17:517–525. doi: 10.1080/13607863.2012.751580. [DOI] [PubMed] [Google Scholar]
  • 44.Kessler RC, Andrews A, Mroczek D, Ustun B, Wittchen HU. The World Health Organization composite international diagnostic interview short-form (CIDI-SF) Int. J. Methods Psychiatr. Res. 1998;7:171–185. doi: 10.1002/mpr.47. [DOI] [Google Scholar]
  • 45.Steffick D. Documentation of affective functioning measures in the health and retirement study, 2000. http://hrsonline.isr.umich.edu/sitedocs/userg/dr-005.pdf (Accessed 2 January 2021).
  • 46.Trainor K, Mallett J, Rushe T. Age related differences in mental health scale scores and depression diagnosis: Adult responses to the CIDI-SF and MHI-5. J. Affect. Disord. 2013;151(2):639–645. doi: 10.1016/j.jad.2013.07.011. [DOI] [PubMed] [Google Scholar]
  • 47.Wen CP, David Cheng TY, Tsai SP, Chan HT, Hsu HL, Hsu CC, Eriksen MP. Are Asians at greater mortality risks for being overweight than Caucasians? Redefining obesity for Asians. Public Health Nutr. 2009;12(4):497–506. doi: 10.1017/S1368980008002802. [DOI] [PubMed] [Google Scholar]
  • 48.Dhawan D, Sharma S. Abdominal Obesity, adipokines and non-communicable diseases. J. Steroid Biochem. Mol. Biol. 2020;203:105737. doi: 10.1016/j.jsbmb.2020.105737. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Rose GA. The diagnosis of ischaemic heart pain and intermittent claudication in field surveys. Bull. World Health Organ. 1962;27:645–658. [PMC free article] [PubMed] [Google Scholar]
  • 50.Achterberg S, Soedamah-Muthu SS, Cramer MJ, Kappelle LJ, van der Graaf Y, Algra A, et al. Prognostic value of the Rose questionnaire: A validation with future coronary events in the SMART study. Eur. J. Prev. Cardiol. 2012;19(1):5–14. doi: 10.1177/1741826710391117. [DOI] [PubMed] [Google Scholar]
  • 51.Rahman MA, Spurrier N, Mahmood MA, Rahman M, Choudhury SR, Leeder S. Rose Angina questionnaire: Validation with cardiologists' diagnoses to detect coronary heart disease in Bangladesh. Indian Heart J. 2013;65(1):30–39. doi: 10.1016/j.ihj.2012.09.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL, Jr, Jones DW, Materson BJ, Oparil S, Wright JT, Jr, Roccella EJ, Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. National Heart, Lung, and Blood Institute; National High Blood Pressure Education Program Coordinating Committee Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension. 2003;42(6):1206–52. doi: 10.1161/01.HYP.0000107251.49515.c2. [DOI] [PubMed] [Google Scholar]
  • 53.Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. Studies of illness in the aged. The index of adl: A standardized measure of biological and psychosocial function. JAMA. 1963;185:914–9. doi: 10.1001/jama.1963.03060120024016. [DOI] [PubMed] [Google Scholar]
  • 54.Lawton MP, Brody EM. Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist. 1969;9(3):179–186. doi: 10.1093/geront/9.3_Part_1.179. [DOI] [PubMed] [Google Scholar]
  • 55.Singh S, Multani S, Verma N. Development and validation of geriatric assessment tools: A preliminary report from Indian population. JESP. 2007;3(2):103–110. [Google Scholar]
  • 56.Blumberg SJ, Bialostosky K, Hamilton WL, Briefel RR. The effectiveness of a short form of the household food security scale. Am. J. Public Health. 1999;89(8):1231–1234. doi: 10.2105/ajph.89.8.1231. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Lee, J., Shih, R.A., Feeney, K., Langa, K.M. Cognitive health of older indians individual and geographic determinants of female disadvantage. https://www.rand.org/content/dam/rand/pubs/working_papers/2011/RAND_WR889.pdf (Accessed 5 June 2021) (2011).
  • 58.Coates J, Frongillo EA, Rogers BL, Webb P, Wilde PE, Houser R. Commonalities in the experience of household food insecurity across cultures: What are measures missing? J. Nutr. 2006;136(5):1438S–1448S. doi: 10.1093/jn/136.5.1438S. [DOI] [PubMed] [Google Scholar]
  • 59.Sethi V, Maitra C, Avula R, Bhalla S. Internal validity and reliability of experience-based household food insecurity scales in Indian settings. Agric. Food Secur. 2017;6:21. doi: 10.1186/s40066-017-0099-3. [DOI] [Google Scholar]
  • 60.Berkman LF, Sekher TV, Capistrant B, Zheng Y. Social networks, family, and care giving among older adults in India. In: Smith JP, Majmundar M, editors. Aging in Asia: Findings From New and Emerging Data Initiatives. The National Academic Press; 2012. pp. 261–278. [PubMed] [Google Scholar]
  • 61.Marsland AL, Gianaros PJ, Abramowitch SM, Manuck SB, Hariri AR. Interleukin-6 covaries inversely with hippocampal grey matter volume in middle-aged adults. Biol. Psychiatry. 2008;64(6):484–490. doi: 10.1016/j.biopsych.2008.04.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Bruening M, Dinour LM, Chavez JBR. Food insecurity and emotional health in the USA: A systematic narrative review of longitudinal research. Public Health Nutr. 2017;20(17):3200–3208. doi: 10.1017/S1368980017002221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Huddleston-Casas C, Charnigo R, Simmons LA. Food insecurity and maternal depression in rural, low-income families: A longitudinal investigation. Public Health Nutr. 2009;12(8):1133–1140. doi: 10.1017/S1368980008003650. [DOI] [PubMed] [Google Scholar]
  • 64.Leung CW, Epel ES, Willett WC, Rimm EB, Laraia BA. Household food insecurity is positively associated with depression among low-income supplemental nutrition assistance program participants and income-eligible nonparticipants. J. Nutr. 2015;145(3):622–627. doi: 10.3945/jn.114.199414. [DOI] [PubMed] [Google Scholar]
  • 65.Laraia BA. Food insecurity and chronic disease. Adv. Nutr. 2013;4(2):203–212. doi: 10.3945/an.112.003277. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Vercammen KA, Moran AJ, McClain AC, Thorndike AN, Fulay AP, Rimm EB. Food security and 10-year cardiovascular disease risk among U.S. adults. Am. J. Prev. Med. 2019;56(5):689–697. doi: 10.1016/j.amepre.2018.11.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Chakraborty R, Kundu J, Jana A. Factors associated with food insecurity among older adults in India: Impacts of functional impairments and chronic diseases. Ageing International, 1–24 (2022).
  • 68.Jackson JA, Branscum A, Tang A, Smit E. Food insecurity and physical functioning limitations among older U.S. adults. Prev. Med. Rep. 2019;14:100829. doi: 10.1016/j.pmedr.2019.100829. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Sreeramareddy CT, Ramakrishnareddy N. Association of adult tobacco use with household food access insecurity: Results from Nepal demographic and health survey, 2011. BMC Public Health. 2017;18(1):48. doi: 10.1186/s12889-017-4579-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Mayer M, Gueorguieva R, Ma X, White MA. Tobacco use increases risk of food insecurity: An analysis of continuous NHANES data from 1999 to 2014. Prev. Med. 2019;126:105765. doi: 10.1016/j.ypmed.2019.105765. [DOI] [PubMed] [Google Scholar]
  • 71.Kim-Mozeleski JE, Poudel KC, Tsoh JY. Examining reciprocal effects of cigarette smoking, food insecurity and psychological distress in the U.S. J. Psychoact. Drugs. 2021;53(2):177–184. doi: 10.1080/02791072.2020.1845419. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Dewing S, Tomlinson M, le Roux IM, Chopra M, Tsai AC. Food insecurity and its association with co-occurring postnatal depression, hazardous drinking, and suicidality among women in peri-urban South Africa. J. Affect. Disord. 2013;150(2):460–465. doi: 10.1016/j.jad.2013.04.040. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

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

Articles from Scientific Reports are provided here courtesy of Nature Publishing Group

RESOURCES