Underweight, Overweight and Anaemia among Persons Aged 60 Years or Older Residing in an Urban Resettlement Colony of Delhi: A Cross-sectional Study
Correspondence Address :
Dr. Sanjeev Kumar Gupta,
E-101, Ansari Nagar (East), New Delhi, India.
E-mail: sgupta_91@yahoo.co.in
Introduction: Nutritional deficiencies are common among elderly person aged 60 years or older. Elderly persons suffer the dual burden of overnutrition and undernutrition. These nutritional disorders can be corrected if diagnosed and managed at the earliest.
Aim: To estimate the prevalence and factors associated with underweight, overweight, obesity and anaemia among elderly persons in an urban resettlement colony of Delhi, India.
Materials and Methods: The present study was a cross-sectional survey in which elderly persons who were residents of Dr. Ambedkar Nagar, an urban resettlement colony in Dakshinpuri Extension of Delhi were recruited. The study period was from December 2019 to March 2020. A pretested semi-structured interview schedule was used to collect socio-demographic details. The anthropometric measurements, namely, height and weight were carried out as per standard practice. Capillary blood haemoglobin level was measured by a digital haemoglobinometer. Chi-square test for distribution and multivariable logistic regression for association were performed.
Results: Data was collected from 959 participants, with a response rate of 91.2%. The prevalence (95%CI) of underweight, overweight and obesity were 15.5% (13.3-18.0), 21.9% (19.3-24.6) and 9.6% (7.7-11.6), respectively. Persons aged 70 years or older and illiterate persons had increased chance of being underweight. Women had increased chance of being overweight or obese. The prevalence of anaemia among participants was 72.1% (95% CI (69.2-74.9), using the cut-off of anaemia as haemoglobin levels <13 gm/dL in men, and <12 gm/dL in women.
Conclusion: The prevalence of underweight, overweight, obesity and anaemia among elderly persons in the study population was high. Community-based measures need to be taken to address them.
Body mass index, Elderly, Malnutrition, Prevalence, Thinness
Malnutrition is a significant public health problem among elderly persons in low-and-middle income countries. It is negatively impacted by physiological changes due to ageing (1). In India, various studies report a high prevalence of underweight, overweight and obesity among elderly persons (2),(3),(4),(5). As per the National Census 2011, 8.6% of the population were aged 60 years and above (6). Aged individuals have increased risk for nutritional imbalance (7). A systematic review on risk factors for malnutrition reported that poor appetite, loss of interest in life, eating dependencies, dementia, cognitive decline, excessive polypharmacy, and general decline in physical health were significantly associated with malnutrition [8-10]. Poor economic capacity and abuse of elderly persons were associated with dietary deficiency of nutrients (11).
Anaemia among elderly is often overlooked in routine clinical and laboratory evaluation, as the presenting symptoms are usually fatigue, weakness and exhaustion (12). These symptoms are frequently thought to be associated with physiological changes due to ageing.Anaemia among elderly persons is due to nutritional deficiencies in two-thirds of the cases, which can be corrected easily if diagnosed early (13). Other reasons are anaemia of chronic disease including chronic kidney disease, or underlying malignancy or parasitic infections or unexplained cause (14). Elderly persons that are residents of urban resettlement colonies are more vulnerable to nutritional anaemia (15),(16). Evidence to this effect of nutritional conditions of the elderly persons in urban slums are insufficient. The associated socio-demographic factors with nutritional problems also require a close investigation.
The study was conducted to estimate the prevalence and socio-demographic factors associated with underweight, overweight, obesity, and anaemia among person aged 60 years or older who were residents of Dr. Ambedkar Nagar, an urban resettlement colony in Dakshinpuri Extension of Delhi, India.
This study was a cross-sectional survey conducted from December 2019 to March 2020. The study site was an urban resettlement colony in Dakshinpuri Extension, Delhi where approximately 2,900 elderly persons resided (17). The ethical approval for the study was accorded by the Institute Ethics Committee (IEC) of All India Institute of Medical Sciences, New Delhi, vide memorandum no. IEC-671/6.09.2019, RP-37/2019. The study was also approved by the Centre for Community Medicine which maintains the computerised Health Management Information System.
Inclusion criteria: Persons aged 60 years and above, and those residing in the study area for atleast six preceding months were included in the study.
Exclusion criteria: Elderly persons who were unable to comprehend or communicate were excluded from the study.
Demographic details of the population were maintained by healthcare workers in a computerised Health Management Information System.This is an in-house health management system by the Centre for Community Medicine authorities. This consists of basic socio-demographic and health details of all the individuals in the urban filed practice area and this was updated annually. The lowest reported prevalence among the three health problems under investigation was for anaemia (20.6%), and the same was used for calculation of required sample size (18),(19),(20). With the assumed absolute precision of 2.5%, and alpha of 5%, the required sample size was 1,047. An allowance for death and migration (15%), and for non response (5%) from previous experience of conducting research in the study area were made. The resulting required final sample size was 1,308 elderly persons. From the sampling frame, through simple random sampling, 1,308 participants were selected.
Socio-demographic details were collected through a self-developed semi-structured interview schedule. It included their age, education, current occupation, type of family, marital status, and economic dependency. Selected participants were paid a house visit by trained non specialist graduate interviewers. The interviewers were trained in administering the interview schedule, measurement of anthropometry, and haemoglobin estimation. Upto a maximum of three home visits were made to contact the participants. After explaining the purpose and procedure of the study, written informed consent was sought from the participants.
Age of the participant was recorded as stated by the participant or based on any valid document, if available. If the source of personal income or any monetary benefit from the social welfare scheme was perceived to be sufficient to maintain himself/herself, then the participant was classified as economically independent. If the same was considered insufficient, then the participant was considered to be economically partially dependent. An economically dependent participant was a person with no personal income or monetary benefit from any social welfare scheme (21).
The body weight and arm span were measured as per standard practice (22). The formula for calculating Body Mass Index (BMI) was: BMI=Weight (kg)/Arm span (m2). In elderly persons, arm span is considered better than height for calculating body mass height as the progression in age causes gradual loss in height due to degenerative osteoporotic changes in bones and decrease in the disc space (23). The BMI was classified as underweight (<18.5 kg/m2), normal (18.5-24.9 kg/m2), overweight (25-29.9 kg/m2), and obese (≥30 kg/m2) as per World Health Organisation (WHO) classification (24).
Haemoglobin was estimated in the capillary blood by using the HemoCue® Hb 201 DM system (HemoCue AB, Sweden). HemoCue® was recommended for point-of-care estimation of haemoglobin, and it was reported to be comparable with other methods of estimation (25). A finger-prick was made and the first two drops of blood was discarded. The subsequent blood drop was collected in a micro-cuvette, and placed in the slot for measurement. The results were available within 80 seconds. The cut-off value of haemoglobin in elderly persons for anaemia was <13 gm/dL in men, and <12 gm/dL in women. Sub-categories of anaemia were as per WHO classification (26). The haemoglobin test results were provided to the participant, and those found anaemic were provided appropriate treatment and referral.
Statistical Analysis
Data collected on paper were scrutinised for completeness and coherence prior to the data entry. Epi Info Version 7.2 (CDC Atlanta Georgia) was used for data entry. The socio-demographic characteristics were reported as proportion or mean. Multivariable logistic regression model was used to assess the association between nutritional status and socio-demographic variables. The p-value <0.05 was considered statistically significant. Stata software version 12.0 was used for analysis. The statistical tests used were Chi-square test and multivariable logistic regression.
Out of 1,308 elderly persons selected for the study, 87 were dead, and 169 had migrated. Of the remaining 1,052 participants, 75 refused to participate, and 18 were non contactable even after three Two regression models were constructed. In the first model, underweight category was the dependent variable and socio-demographic characteristics were the independent variables (Table/Fig 3). Elderly persons aged ≥70 years had 1.9 (95% CI 1.2-3.0) times increased chance of underweight than 60-64 years in the crude model. In the multivariable model, women had 40% decreased chance of underweight compared to men. Illiterate had 1.7 (95% CI 1.1-2.6) times and ≥70 years had 2.0 (95% CI 1.2-3.3) times increased chance of underweight compared to literate and 60-64 years respectively. All these associations were statistically significant.
In the second model, overweight and obese categories were combined as dependent variable and sociodemographic characteristics as the independent variables (Table/Fig 4). In the crude model, overweight/obese was higher among women; elderly persons aged ≥70 years; illiterates; and economically dependent elderly persons. In the multivariable model, women had four times AOR=4.0, 95% CI 2.6-6.1) increased chance of being overweight/obese. Elderly persons aged ≥70 years had 60% decreased risk (AOR=0.4, 95% CI 0.3-0.7) of being overweight/obese. All these associations were statistically significant. No significant association was found for education, current occupation, type of family, marital status and economic dependency.
Of the 959 participants, haemoglobin was measured for 958 (99.9%) participants. The prevalence of anaemia (95% CI) was 72.1% (69.2%-74.9%). The prevalence of mild, moderate and severe anaemia were 26.9%, 37.7% and 7.5%, respectively (Table/Fig 5). The mean±SD of haemoglobin levels among the study participants was 11.1±2.2 g/dL (Table/Fig 6).
Logistic regression analyses were conducted to determine the association of anaemia with socio-demographic and anthropometric variables (Table/Fig 7). None of the socio-demographic or anthropometric variables showed a significant association with anaemia in the crude or multivariable model. There was no significant association between BMI, and anaemia in the crude and multivariable model.
The prevalence and socio-demographic factors associated with underweight, overweight/obese, anaemia were estimated among elderly persons residing in an urban resettlement colony of Delhi. This study found that the prevalence of underweight, overweight and obesity were 15.5%, 21.9% and 9.6%, respectively.
In a study by Rajkamal R et al., among elderly population in an urban area of Puducherry, the reported prevalence of overweight and obesity were 41.4% and 4.5%, respectively (2). In their study, religion, occupation, smoking and alcohol consumption were found to be significantly associated with overweight/obesity. In present study, women had four times increased risk being overweight/obesity and elderly persons aged 70 years and above had 40% decreased chance of being overweight/obese.
A community-based cross-sectional study conducted among elderly persons in Chandigarh city by Swami HM et al., found that the prevalence of underweight, overweight and obesity were 14.4%, 33.4% and 7.5%, respectively (3). Their observation that overweight/obesity was higher among women, was similar to present study. A study in the same setting in 2015 reported the prevalence of underweight, overweight and obesity as 20.8%, 19.4% and 6.6%, respectively (4). Elderly women had lower risk of being underweight, which was similar to the findings of present study.
Mathew AC et al., in a study on elderly persons living in urban Coimbatore reported that 19.5% were malnourished, and 24.7% were at risk of malnutrition (5). They found no association of malnutrition with lifestyle, somatic or functional characteristics. Of the total participants, 55.8% were normal for nutritional status using Mini Nutritional Assessment questionnaire.
In present study, the overall prevalence of anaemia was 72.1%. A study conducted by Vadakattu SS et al., among urban elderly persons in Hyderabad, the reported prevalence of anaemia was 20.6%; and it increased with age. The haemoglobin was estimated using Cyanmethemoglobin method (20). In the present study, there was no association between anaemia and age of the participants. In a study by Kant S et al., among adult men of rural Haryana, the prevalence of anaemia among adults aged 60 years and above was 46.8%. They found a positive association with age and chronic diseases. HemoCue® was used in the estimation of haemoglobin (27).
A study conducted by Malhotra VM et al., among elderly persons of rural Nalgonda, Telangana, reported that the prevalence of anaemia among adults aged 50 years and above was 27.8%. The study found a significant association with females, increasing age, non use of footwear, excessive alcohol consumption and history of chronic blood loss. Haemoglobin levels were measured by Sahli’s technique. The low prevalence of anaemia could be due to low sensitivity of the method used for haemoglobin estimation (28).
Agarwalla R et al., Kamrup, Assam reported that the prevalence of anaemia among elderly persons was 45.5%. The study found a significant association with age, gender, calorie intake, type of diet, iron supplementation, and worm infestation. Sahli’s technique was used for the estimation of haemoglobin level (29). A study conducted by Gonmei Z et al., in slums of West Delhi reported that the prevalence of anemia among elderly persons aged 60 years and above was 57.8%. They had estimated the haemoglobin level by direct cyanmethaemoglobin method (30).
Sudarshan BP and Chethan TK conducted a study in rural Puducherry. The reported prevalence of anaemia among elderly persons was 96.0%.They found a significant association with females and dependent elderly persons. Method used for haemoglobin estimation was not mentioned in the study (31). A study conducted in urban slums of Kochi, Kerala by Retnakumar C et al., that reported the prevalence of anaemia among elderly persons was 60.6%, and women had higher chance of having anaemia. HemoCue® was used for the estimation of haemoglobin (15).
Another study conducted by Lamba R et al., in the urban slums of Meerut, Uttar Pradesh reported that the prevalence of anaemia among elderly persons was 49.5%. They found a significant association with lower socio-economic status, unemployed and chronic diseases like chronic obstructive pulmonary disease. In this study, haemoglobin was estimated using paper chromatography method (sensitivity 56%) using the Haemo Check Rapid Diagnostic Kit. Low prevalence of anaemia in this study could be due to method used for haemoglobin estimation (16).
A study conducted by Gupta A et al., in Nainital, Uttarkhand reported the prevalence of anaemia among elderly persons as 92.1%. Anaemia was significantly associated with females, unemployed, illiterates, participants reporting hyperacidity, those who had not utilised health facility and lower intake of iron and vitamin C. They used cyanmethaemoglobin method for estimation of haemoglobin (32).
A study conducted by Gupta A et al., in Nainital, Uttarkhand reported the prevalence of anaemia among elderly persons as 92.1%. Anaemia was significantly associated with females, unemployed, illiterates, participants reporting hyperacidity, those who had not utilised health facility and lower intake of iron and vitamin C. They used cyanmethaemoglobin method for estimation of haemoglobin (32).
The study design, i.e. cross-sectional community-based survey, and good response rate are some of the strengths of the study. The interviewers were trained in data collection which enhanced the reliability of information. Haemoglobin was measured using the standardised point-of-care test.
Limitation(s)
The limitation is that being a cross-sectional study, temporality of the findings could not be established. Whether the determinants that studied were the precursors/causative agents for the outcome of interest could not be demonstrated beyond doubt.
There was a dual burden of underweight and overweight among the elderly persons residing in this resettlement colony. The prevalence of underweight increased with increasing age. Women had increased risk of being overweight/obese. In addition to the dual burden of malnutrition, the overall prevalence of anaemia among elderly persons was 72.1%. These findings recommend an effective primary care screening and management among elderly persons in urban resettlement areas.
1 World Health Organisation. World report on ageing and health. Geneva. WHO: 2015. n 2021 May 21]. 2 Rajkamal R, Singh Z, Stalin P, Muthurajesh E. Prevalence and determinants of overweight and obesity among elderly population in an urban area of Puducherry. Int J Med Sci Public Health. 2015;4(3):369-72. [crossref] 3 Swami HM, Bhatia V, Gupta A, Bhatia S. An epidemiological study of obesity among elderly in Chandigarh. Indian J Commun Med. 2005;30(1):11. 4 Goswami A, Nongkynrih B, Kalaivani M, Pandav C. Double burden of malnutrition among elderly population of Delhi. Indian J Commun Health. 2016;28:324-30. 5 Mathew AC, Das D, Sampath S, Vijayakumar M, Ramakrishnan N, Ravishankar SL. Prevalence and correlates of malnutrition among elderly in an urban area in Coimbatore. Indian J Pub Health. 2016;60:112-17. [crossref] [PubMed] 6 Chandramouli, C. (2011) Population Enumeration data (final population) India: Registrar General and Census Commissioner of India. [Accessed on 2021 May 21]. 7 Andrès E, Serraj K, Federici L, Vogel T, Kaltenbach G. Anemia in elderly patients: New insight into an old disorder. Geriatr Gerontol Int. 2013;13:519-27. [crossref] [PubMed] 8 Fávaro-Moreira NC, Krausch-Hofmann S, Matthys C, Vereecken C, Vanhauwaert E, Declercq A, et al. Risk factors for malnutrition in older adults: A systematic review of the literature based on longitudinal data 123. Adv Nutr. 2016;7:507-22. [crossref][PubMed] 9 Kushwaha S, Khanna P, Srivastava R, Jain R, Singh T, Kiran T. Estimates of malnutrition and risk of malnutrition among the elderly (≥60 years) in India: A systematic review and meta-analysis. Ageing Research Reviews. 2020;63:101137. [crossref] [PubMed] 10 Besora-Moreno M, Llauradó E, Tarro L, Solà R. Social and economic factors and malnutrition or the risk of malnutrition in the elderly: A systematic review and meta-analysis of observational studies. Nutrients. 2020;12:E737. [crossref] [PubMed] 11 Krug E, Dahlberg LL, Mercy J, Zwi AB, Lozano R, editors. World report on violence and health. Geneva: World Health Organization; 2002;360(9339):1083-88. n 2021 Sep 10]. [crossref] [PubMed] 12 Gómez Ramírez S, Remacha Sevilla ÃF, Muñoz Gómez M. Anaemia in the elderly. Med Clin (Barc). 2017;149(11):496-03. [crossref] 13 Tay HS, Soiza RL. Systematic review and meta-analysis: What is the evidence for oral iron supplementation in treating anaemia in elderly people? Drugs Aging. 2015;32:149-58. [crossref] [PubMed] 14 Goodnough LT, Schrier SL. Evaluation and management of anemia in the elderly. Am J Hematol. 2014;89:88-96. [crossref] [PubMed] 15 Retnakumar C, Chacko M, Ramakrishnan D, George LS, Krishnapillai V. Prevalence of anemia and its association with dietary pattern among elderly population of urban slums in Kochi. J Family Med Prim Care. 2020;9(3):1533-37. [crossref] [PubMed] 16 Lamba R, Agarwal A, Rana R, Agarwal V. Prevalence of anemia and its correlates among elderly population of an urban slum in Meerut. Journal of the Indian Academy of Geriatrics. Medknow Publications; 2019;15:109. [crossref] 17 All India Institute of Medical Sciences, 65th Annual Report: 2020-2021 [Internet]. New Delhi: All India Institute of Medical Sciences; 2022 Jan p. 1056. [Accessed on 2022 Apr 28]. Available from: https://www.aiims.edu/images/pdf/annual_ reports/annualreport-e-18-1-22.pdf. 18 Binu J, Harnagle R. A study on the prevalence of overweight and obesity and its influencing factors among rural geriatric population in Kerala. Int J Curr Microbiol App Sci. 2014;3(9):284-93. 19 Kalaiselvi S, Arjumand Y, Jayalakshmy R, Gomathi R, Pruthu T, Palanivel C. Prevalence of under-nutrition, associated factors and perceived nutritional status among elderly in a rural area of Puducherry, South India. Arch Gerontol Geriatr. 2016;65:156-60. [crossref] [PubMed] 20 Vadakattu SS, Ponday LR, Nimmathota A, Nagalla B, Kondru DS, Undrajavarapu P, et al. Prevalence of nutritional anemia and hyperhomocysteinemia in urban elderly. Indian J Clin Biochem. 2019;34(3):330-35. [crossref] [PubMed] 21 Goswami AK, Ramadass S, Kalaivani M, Nongkynrih B, Kant S, Gupta SK. Awareness and utilization of social welfare schemes by elderly persons residing in an urban resettlement colony of Delhi. J Family Med Prim Care. 2019;8(3):960-65. [crossref] [PubMed] 22 World Health Organization. Physical Status- the Use and Interpretation of Anthropometry: Report of a WHO expert committee: No. 854. Geneva: World Health Organization; 1995. [Accessed on 2021 Jul 24]. 23 Goswami AK, Kalaivani M, Gupta SK, Nongkynrih B, Pandav CS. Relationship between height and arm span of elderly persons in an urban colony of New Delhi. Indian Journal of Public Health. 2018;62:159-62. 24 WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet (London, England). 2004;363(9403):157-63. [crossref] 25 Nkrumah B, Nguah SB, Sarpong N, Dekker D, Idriss A, May J, et al. Hemoglobin estimation by the HemoCue® portable hemoglobin photometer in a resource poor setting. BMC Clinical Pathology. 2011;11:5. [crossref] [PubMed] 26 World Health Organization. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. Vitamin and Mineral Nutrition Information System. Geneva: World Health Organization; 2011. Accessed n 2021 Oct 6]. 27 Kant S, Kumar R, Malhotra S, Kaur R, Haldar P. Prevalence and determinants of anemia among adult males in a rural area of Haryana, India. J Epidemiol Glob Health. 2019;9(2):128-34. [crossref] [PubMed] 28 Malhotra VM, Kabra PR, Bhayya S, Malhotra R. Prevalence and correlates of anemia among elderly population of rural Nalgonda: A cross-sectional analytic study. Public Health Review: Int J Public Health Res. 2016;3:168-73. [crossref] 29 Agarwalla R, Saikia A, Parashar M, Pathak R, Islam F. Assessment of prevalence of anemia in and its correlates among community-dwelling elderly of Assam, India: A cross-sectional study. In J Nutr, Pharmacol Neuro Sci. 2016;6(1):23-27. [crossref] 30 Gonmei Z, Dwivedi S, Toteja GS, Singh K, Vikram NK, Bansal PG. Anemia and vitamin B12 deficiency in elderly. Asian Journal of Pharmaceutical and Clin Res. 2018;402-04. [crossref] 31 Sudarshan BP, Chethan TK. A study to assess the prevalence of anemia and activities of daily living among elderly population residing in a South Indian rural community. Int J Comm Med Pub Health. 2017;3:437-41. [crossref] 32 Gupta A, Ramakrishnan L, Pandey RM, Sati HC, Khandelwal R, Khenduja P, et al. Risk factors of anemia amongst elderly population living at high-altitude region of India. J Family Med Prim Care. 2020;9:673-82. [crossref] [PubMed] 33 Singh T, Nagesh S, Ray TK. Magnitude and correlates of anemia in elderly women of a resettlement colony of delhi. J Midlife Health. 2018;9:21-25. [crossref] [PubMed]
DOI: 10.7860/JCDR/2022/55915.16647
Date of Submission: Feb 25, 2022
Date of Peer Review: Mar 25, 2022
Date of Acceptance: May 10, 2022
Date of Publishing: Jul 01, 2022
AUTHOR DECLARATION:
• Financial or Other Competing Interests: All India Institute of Medical Sciences, New Delhi vide
memorandum no. F.8-747/A-747/2019/RS dated 25 October 2019.
• Was Ethics Committee Approval obtained for this study? Yes
• Was informed consent obtained from the subjects involved in the study? Yes
• For any images presented appropriate consent has been obtained from the subjects. NA
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