Prevalence of Anemia and Its Associated Factors among Urban Indian Elderly Population: Findings from Cross-sectional Study

Abstract

Background:

Anemia is a global public health concern with adverse effects on health and socioeconomic development. Among the elderly, it is often underdiagnosed and undertreated despite being a marker of physiological decline and morbidity. With India undergoing demographic transition, the burden among the elderly is expected to increase. This study aimed to estimate the prevalence and associated factors of anemia among the urban Indian elderly population.

Methods:

A population-based cross-sectional study was conducted from January 2019 to June 2020 among 250 elderly individuals (≥60 years) in the urban field practice area of a medical college. Data on sociopersonal characteristics, dietary and personal habits, and comorbidities were collected using a pretested semistructured proforma. Hemoglobin was estimated using the HemoCue Hb 301 system, and anemia was classified as per WHO criteria. Data were analyzed using Chi-square test, t-test, and multivariable logistic regression.

Results:

The prevalence of anemia was 38.4%. Multivariable logistic regression analysis showed that increasing age, female sex, dependence in activities of daily living, and consumption of two or fewer meals per day were independently associated with anemia. Pallor was significantly associated with lower mean hemoglobin levels (P < 0.001).

Conclusion:

More than one-third of the urban Indian elderly population had anemia. Increasing age, female sex, dependence in activities of daily living, and consumption of two or fewer meals per day were independently associated with anemia. These findings highlight the need for routine screening, nutritional assessment, and functional evaluation among the elderly population and support greater inclusion of elderly in national demographic and health surveys and future anemia prevention and control initiatives.

INTRODUCTION

Anemia is an important determinant of nutritional status and is considered a global public health problem affecting all countries, with negative consequences for human health and socioeconomic development.[1] It is a condition in which the number of red blood cells or their oxygen-carrying capacity is insufficient to meet physiological needs, which vary according to age and sex.[2]

Anemia may occur at all ages and in either sex; however, the elderly are among the most vulnerable. The relationship between ageing and anemia is complex because of its multifactorial causation and the increased heterogeneity of social and biological risk factors. In the elderly, increased comorbidity poses difficulty in establishing whether anemia is a marker of disease burden or a mediator in the causal pathway leading to adverse health events.[3]

Anemia is common among the elderly population, and its prevalence increases with age.[4] In the elderly, it is caused by nutritional deficiencies of iron, folic acid, vitamin B₁₂, and vitamin A; inflammation due to chronic diseases; parasitic infestations; and malignancies. Apart from these causes, age-related alterations in the hematopoietic system also contribute to its burden.[5] Although clinical evaluation of anemia requires multiple laboratory tests for diagnosis, at the population level, estimation of hemoglobin concentration is a reliable indicator of anemia.

Anemia among the elderly is often underdiagnosed and undertreated despite substantial evidence that low hemoglobin levels are associated with physiological decline, impaired physical function, cognitive impairment, functional decline, frailty, falls, infections, hospitalization, cardiovascular morbidity, and poor quality of life.[6]

In India, the prevalence of anemia among the elderly population has been reported to range from 21% to 92%.[7] The wide variation may be attributed to differences in study settings, population characteristics, and diagnostic criteria. These findings indicate that anemia is an important public health concern among the elderly. Despite its public health significance, the elderly are not specifically covered for routine anemia assessment in major national demographic and health surveys such as the National Family Health Survey (NFHS). Furthermore, although the Anaemia Mukt Bharat (AMB) strategy has strengthened anemia prevention and control among several vulnerable population groups in India, the elderly are not included as a specific target population under the program.[8]

Presently, India is undergoing a demographic transition and is considered an ageing nation. Currently, 8.1% of the population comprises elderly individuals, and this proportion is projected to increase to nearly 20% by 2050. Such a trend is likely to increase the burden of anemia in the future.[9,10] Findings from the Longitudinal Ageing Study in India (LASI) Wave-1 have also highlighted the increasing burden of chronic diseases, functional impairment, and nutritional problems among the elderly, emphasizing the need for greater attention to anemia in this population.[11]

There is a scarcity of studies on anemia involving only the elderly population, particularly from urban India. Considering these facts, the present study was undertaken to estimate the prevalence of anemia and identify the factors associated with anemia among the urban elderly population. The findings from this study may provide evidence to support planning of appropriate preventive and therapeutic interventions.

MATERIALS AND METHODS

Study design and settings

A population-based cross-sectional study was conducted in a locality which was selected using a lottery method out of eight localities with a total population of 43,059 under urban field practice areas of a Medical College, Bengaluru, Karnataka, India, from January 2019 to June 2020. Although the study period extended until June 2020, participant recruitment and data collection were completed before the implementation of nationwide COVID-19 restrictions in India. Consequently, the COVID-19 pandemic did not influence participant recruitment, data collection, or study procedures. This study was commenced after approval from the Institutional Ethics Committee (IEC ID number-KIMS/IEC/D-103/2018), and informed written consent was obtained from all the elderly subjects. For participants who were unable to read or write, the participant information sheet was read aloud in the local language. Written informed consent was documented using the participant’s left thumb impression in the presence of an impartial witness.

Participant confidentiality was maintained by assigning unique identification numbers. Personal identifiers were removed from the analytical dataset, and electronic data were stored in password-protected files accessible only to the investigators.

Study participants

Elderly subjects aged ≥60 years residing in the locality for more than 6 months and willing to participate in the study were included. The elderly with a history of blood disorders, or history of blood transfusion, or intake of iron and folic acid or vitamin B12 in the past 6 months based on medical records and those with severe illness, cognitive impairment, speech, and hearing difficulty were excluded.

Sample size and data collection

The sample size was estimated using the formula n = (Zα/2) ²pq/d²). The prevalence of anemia “p” among elderly was 20.6% with a precision of 5%. The sample size was estimated to be 246 and approximated to 250.[10]

In the selected locality, totally 446 elderly were identified by conducting house-to-house survey. Enquiry was made regarding their willingness and eligibility to participate in the study using inclusion and exclusion criteria. Finally, 400 elderly were line listed to form a sampling frame. From this sampling frame, the required sample size of 250 participants was selected using simple random sampling with computer-generated random numbers. The study participant selection process is shown in Figure 1.

Figure 1.

Flow diagram of participant selection and inclusion in the study

Study procedure and tools

In the house visit, the purpose of the study was explained to the elderly subject and written informed consent was obtained. Data were collected regarding sociopersonal characteristics (age, sex, religion, education, employment status, marital status). Standard of living index (SLI) was assessed using the 11-item NFHS Standard of Living Index (score range: 0–67). Based on the total score, households were categorized as low (0–14), medium (15–24), and high (25–67) standard of living. For statistical analysis, the low and medium categories were combined because of the small number of participants in the medium category.[12]

Functional status was assessed using the Katz Index of Activities of Daily Living (ADL). Study participants with a score of 6 were considered independent, while those with scores ≤5 were classified as dependent for statistical analyses.[13] Detailed history on dietary habits (type of diet, number of meals per day, consumption of iron-rich vegetables and fruits) and personal habits (use of footwear, alcohol consumption) was obtained. Comorbidity status was ascertained along with usage of medication based on medical records. All the data were collected using a pretested, semistructured proforma by an interview method. The semistructured proforma was developed after an extensive review of the literature. It was reviewed by experts in Community Medicine for content validity. The revised proforma was pilot-tested among 30 elderly individuals from a nonstudy area, and minor modifications were made to improve the clarity, sequence, and comprehensibility of the questions before commencement of the study. The final proforma is provided as Supplementary File 1.

Religion was categorized as Hindu and non-Hindu due to small numbers in individual non-Hindu groups, which precluded meaningful subgroup analysis. The non-Hindu category included Muslims and Christians.

Clinical examination for the assessment of anemia was performed, and hemoglobin concentration was estimated using the HemoCue Hb 301 system, a portable hemoglobinometer consisting of a specially designed photometer and disposable microcuvettes. Hemoglobin estimation was performed using 10 μL of capillary blood obtained by finger-prick, following the manufacturer’s instructions. In this method, red blood cells are lysed, and hemoglobin is converted to azide methaemoglobin, allowing direct photometric measurement within 30–50 seconds. The HemoCue Hb 301 system is simple to operate under field conditions and has been widely used for hemoglobin estimation in large-scale population-based surveys and public health programs in India, including the NFHS-5[14] and AMB.[8] Furthermore, previous validation studies have demonstrated good agreement between capillary HemoCue measurements and venous laboratory hemoglobin measurements, supporting its use in community-based screening and epidemiological studies.[15,16]

Anemia was assessed based on WHO classification, where ≥13 g/dl for men and ≥12 g/dl for women is considered as normal, with mild (11–12.9 g/dl for men and 11–11.9 g/dl for women), moderate (8–10.9 g/dl), and severe (<8 g/dl) anemia being the other categories.[17] Elderly subjects diagnosed with anemia were made aware of severity, and in case of mild and moderate anemia, health education and a course of iron and folic acid tablets were given. In severe cases, subjects were referred to higher centers for further evaluation and management.

Statistical analysis

Data were entered into Microsoft Excel and analyzed using IBM SPSS Statistics for Windows, Version 29.0. Descriptive statistics are presented as frequencies, percentages, means, and standard deviations, as appropriate. Associations between categorical variables and anemia were assessed using the Chi-square test, while differences in mean hemoglobin concentration between study participants with and without pallor, stratified by sex, were assessed using the independent samples t-test. Variables for the multivariable logistic regression model were selected based on clinical and epidemiological relevance, biological plausibility, and evidence from previous literature, with age and sex retained irrespective of statistical significance. Multicollinearity among the independent variables was assessed before model fitting, and no significant multicollinearity was observed. Model calibration was evaluated using the Hosmer–Lemeshow goodness-of-fit test, and model discrimination was assessed using the c-statistic (area under the receiver operating characteristic curve). Adjusted odds ratios (aORs) with 95% confidence intervals (CIs) were reported. A two-sided P value <0.05 was considered statistically significant. This study was reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines, and the completed STROBE checklist is provided as Supplementary File 2.

RESULTS

A total of 446 older adults (≥60 years) were enumerated in the selected urban locality. Of these, 400 fulfilled the eligibility criteria and consented to participate, forming the sampling frame. Using simple random sampling, 250 participants were selected, all of whom completed the study and were included in the final analysis [Figure 1].

The present study consists of 250 elderly subjects aged ≥60 years, out of which 103 (41.2%) were males and 147 (58.8%) were females. The mean age of the subjects was 69.7 ± 7.2 years with the range from 60 to 89 years.

Sociopersonal characteristics of the elderly subjects showed that 144 (57.6%) were in the age group of 60–69 years. Among the study participants, 103 (41.2%) were Hindus, 136 (54.4%) were Muslims, and 11 (4.4%) were Christians. For bivariate analysis, Muslim and Christian participants were combined into a single non-Hindu category. A total of 160 (64.0%) participants were illiterate, 164 (65.6%) were employed, 137 (54.8%) were married, and 179 (71.6%) belonged to the low/medium Standard of Living Index group.

The prevalence of anemia among elderly was 38.4% [95% confidence interval (CI) 32.3–44.7%]. Among participants with anemia, 37 (38.5%; 95% CI: 28.8%–49.0%) had mild anemia, 40 (41.7%; 95% CI: 31.7%–52.2%) had moderate anemia, and 19 (19.8%; 95% CI: 12.4%–29.2%) had severe anemia.

The prevalence of anemia was higher among females (45.6%, 95% CI: 37.4%–54.0%) than among males (28.2%, 95% CI: 19.7%–37.9%).

In this study, sociopersonal characteristics such as age (P = 0.003), sex (P = 0.005), use of footwear (P = 0.001), alcohol consumption (P = 0.02), and functional status based on activities of daily living (ADL) (P = 0.001) were significantly associated with anemia among the elderly in the bivariate analysis. Religion, education, employment status, marital status, comorbidity status, and standard of living index were not significantly associated with anemia [Table 1].

Table 1.

Association between anemia and sociopersonal characteristics

Variables	Anemia (n=96)	No Anemia (n=154)	P
Age (years)
60-69	44 (30.6)	100 (69.4)	0.003
≥70	52 (49.1)	54 (50.9)
Sex
Male	29 (28.2)	74 (71.8)	0.005
Female	67 (45.6)	80 (54.4)
Religion
Hindu	47 (45.6)	56 (54.4)	0.1
Non-Hindu	49 (33.3)	98 (66.7)
Education
Illiterate	59 (36.9)	101 (63.1)	0.5
Literate	37 (41.1)	53 (58.9)
Employment status
Employed	67 (40.8)	97 (59.2)	0.2
Unemployed	29 (33.7)	57 (66.3)
Marital status
Married	49 (35.7)	88 (64.3)	0.3
Single	47 (41.6)	66 (58.4)
Use of footwear
Yes	75 (34.4)	143 (65.6)	0.001
No	21 (65.6)	11 (34.4)
Alcohol Consumption
Yes	20 (27.8)	52 (72.2)	0.02
No	76 (42.7)	102 (57.3)
Functional status by ADL
Independent	64 (32.8)	131 (67.2)	0.001
Dependent	32 (58.2)	23 (41.8)
Comorbidity Status
No	44 (33.3)	88 (66.7)	0.08
Yes	52 (44.1)	66 (55.9)
Standard of Living Index
Low/medium	75 (41.9)	104 (58.1)	0.07
High	21 (29.6)	50 (70.4)

P<0.05 is considered significant for analysis done using Chi square test

Among the dietary factors, the number of meals consumed per day (P = 0.001) and type of diet (P = 0.007) were significantly associated with anemia, whereas the consumption of iron-rich vegetables and iron-rich fruits was not significantly associated [Table 2].

Table 2.

Association between anemia and dietary habits

Variables	Anemia (n=96)	No anemia (n=154)	P
Number of meals
≤ 2	53 (50.9)	51 (49.1)	0.001
>2	43 (29.5)	103 (70.5)
Type of diet
Vegetarian	22 (57.9)	16 (42.1)	0.007
Mixed	74 (34.9)	138 (65.1)
Consuming Iron rich vegetables
< 3 times/week	58 (34.5)	110 (65.5)	0.07
≥ 3 times/week	38 (46.3)	44 (53.7)
Consuming Iron-rich fruits
Yes	61 (35.5)	111 (64.5)	0.2
No	35 (44.9)	43 (55.1)

P<0.05 is considered significant for analysis done using Chi square test

Multivariable logistic regression analysis showed that age ≥70 years (aOR: 2.4; 95% CI: 1.1–5.2), female sex (aOR: 5.3; 95% CI: 2.1–13.6), dependence in activities of daily living (aOR: 4.3; 95% CI: 1.7–10.9), and consumption of ≤2 meals per day (aOR: 2.4; 95% CI: 1.1–5.4) were independently associated with anemia. Use of footwear, alcohol consumption, standard of living index, type of diet, and consumption of iron-rich vegetables were not independently associated with anemia after adjustment [Table 3].

Table 3.

Association between individual variable and anemia by multivariable logistic regression

Variables	Anemia (n=96)	No anemia (n=154)	Crude OR [95% CI]	Adjusted OR [95% CI]
Age
60-69	44 (30.6)	100 (69.4)	1 (ref)	1 (ref)
≥70	52 (49.1)	54 (50.9)	2.2[1.3-3.6]	2.4[1.1-5.2]
Sex
Male	29 (28.2)	74 (71.8)	1 (ref)	1 (ref)
Female	67 (45.6)	80 (54.4)	2.1[1.2-3.6]	5.3[2.1-13.6]
Use of footwear
Yes	75 (34.4)	143 (65.6)	1 (ref)	1 (ref)
No	21 (65.6)	11 (34.4)	3.6[1.6-7.9]	0.5[0.2-1.4]
Alcohol Consumption
No	76 (42.7)	102 (57.3)	1 (ref)	1 (ref)
Yes	20 (27.8)	52 (72.2)	1.9[1.1-3.5]	1.6[0.6-4.8]
Functional status by ADL
Independent	64 (32.8)	131 (67.2)	1 (ref)	1 (ref)
Dependent	32 (58.2)	23 (41.8)	2.8[1.5-5.2]	4.3[1.7-10.9]
Standard of Living Index
High	21 (29.6)	50 (70.4)	1 (ref)	1 (ref)
Low/medium	75 (41.9)	104 (58.1)	1.7[0.9-3.1]	1.4[0.7-2.7]
Type of diet
Mixed	74 (34.9)	138 (65.1)	1 (ref)	1 (ref)
Vegetarian	22 (57.9)	16 (42.1)	2.5[1.2-5.2]	0.4[0.2-1.2]
Number of meals
>2	43 (29.5)	103 (70.5)	1 (ref)	1 (ref)
≤ 2	53 (50.9)	51 (49.1)	2.5[1.5-4.2]	2.4[1.1-5.4]
Consuming Iron-rich vegetables
≥3 times/week	38 (45.8)	45 (54.2)	1 (ref)	1 (ref)
< 3 times/week	58 (34.7)	109 (65.3)	0.6[0.3-1.1]	0.8[0.4-1.8]

Note: (ref) – Reference category

The goodness-of-fit of the multivariable logistic regression model was assessed using the Hosmer–Lemeshow goodness-of-fit test (χ² =4.30, P = 0.829), and model discrimination was assessed using the c-statistic (area under the receiver operating characteristic curve = 0.786).

Among participants with anemia, 75.0% had clinical pallor, and the proportion of participants with pallor increased with increasing severity of anemia.

The mean hemoglobin concentration was significantly lower among participants with pallor than among those without pallor in both men (12.4 ± 2.7 vs. 13.8 ± 1.2 g/dL; P = 0.0009) and women (11.0 ± 2.4 vs. 12.4 ± 1.4 g/dL; P < 0.001) [Table 4].

Table 4.

Comparison of mean hemoglobin concentration by pallor status stratified by sex

Sex	Pallor Mean Hb (g/dL)±SD	No pallor Mean Hb (g/dL)±SD	Mean Difference (95% CI)	P
Men	12.4±2.7	13.8±1.2	−1.4 (−2.1 to −0.6)	0.0009
Women	11.0±2.4	12.4±1.4	−1.5 (−2.1 to −0.8)	<0.001

Independent samples t-test comparing mean hemoglobin concentration between participants with and without pallor within each sex

DISCUSSION

Anemia among the elderly is an important marker of physiological decline and is associated with adverse health outcomes, leading to poor quality of life. Despite its public health and clinical significance, the elderly population is not specifically covered for routine anemia assessment in most national demographic, health, and nutrition surveys in India. In this context, the present study aimed to estimate the prevalence of anemia and identify the factors associated with anemia among the urban Indian elderly population.

This study documents the prevalence of anemia among elderly population as 38.4%. Similarly, the Longitudinal Ageing Study in India (LASI) reported that approximately one-third of older adults were anemic, indicating that anemia remains a major public health problem among the ageing population in India,[11] and a previous study in India observed the highest prevalence of 65%.[7] Contrary to these findings, studies by Afaghi H and Ni W observed prevalences of 11.1% and 15.4%, respectively.[18,19] Studies from western countries also observed the prevalence of anemia ranging from 10% to 24%.[20] The observed variation in prevalence across studies may be attributed to differences in age and sex distribution, study design, sample size, environmental factors, comorbidity profile, and genetic susceptibility. The relatively higher prevalence reported in Indian studies may partly be explained by dietary patterns, including lower consumption of animal-source foods and greater reliance on cereal-based diets, which may reduce the bioavailability of iron and vitamin B12. Similar observations have been reported previously.[21] Urbanization and changing dietary practices, including greater consumption of processed foods, may further contribute to inadequate micronutrient intake among the elderly population.

In the present study, increasing age was independently associated with anemia. Similar results were observed in previous studies.[7,19,22] This indicates an increase in prevalence with an increase in age. Evidence has shown that cellular and molecular damage for hematopoietic stem cells (HSCs) occurs with ageing due to replacement of hematopoietic tissue with adipose tissue and alternation in transcriptional and epigenetic profiles.[23] These findings suggest the need for regular screening as age advances.

Female sex was also found to be independently associated with anemia. The possible reasons for high prevalence of anemia among females could be due to women’s lower baseline of iron level during reproductive age and age-related changes in hemopoeitic system and nutrient absorption. These findings are in line with a study by Abdulwahed E and Greenblum G.[24,25]

Dependence in activities of daily living (ADL) was independently associated with anemia. Similar findings have been reported by a previous study.[22] Anemia can impair functional capacity through fatigue, reduced exercise tolerance, muscle weakness, and dyspnea, leading to reduced physical performance and greater dependence in ADL among elderly.[26] These findings highlight the importance of routine assessment of functional status during anemia screening among the Indian elderly population.

Dietary factors such as consumption of two or fewer meals per day and vegetarian diet were associated with anemia in the bivariate analysis. However, after adjustment for potential confounders, only lower meal frequency remained independently associated with anemia. Reduced meal frequency may reflect inadequate overall dietary intake and lower consumption of essential micronutrients required for erythropoiesis, including iron, folate, and vitamin B12. Similar observations have been reported in previous studies.[18,27] These findings highlight the need to improve dietary practices among the elderly population. Enhancing both the quantity and quality of diet is essential, and further research is required to better understand the relationship between anemia and dietary habits in this group.

Study participants with clinical pallor had significantly lower mean hemoglobin concentrations than those without pallor in both men and women. There is a paucity of evidence relating clinical pallor to hemoglobin concentration among the Indian elderly population, which warrants further investigation.

The present study was limited by sample size, although it had a robust sampling design, and the underlying causes of anemia could not be determined precisely due to its community-based, cross-sectional nature. In addition, exclusion of study subjects on iron, folate, or vitamin B12 supplementation in the past 6 months may have resulted in underestimation of the true prevalence of anemia. Multiple statistical comparisons were performed; therefore, some of the observed associations may have occurred by chance and should be interpreted with caution until confirmed in larger prospective studies.

The present study highlights the need for larger multicentric prospective studies to improve understanding of the epidemiology and determinants of anemia among elderly in India. Greater inclusion of elderly in national demographic and health surveys, such as the NFHS, would provide more comprehensive estimates of the burden of anemia in this population. These findings also support consideration of elderly in future anemia prevention and control initiatives, including AMB, based on emerging evidence.

CONCLUSIONS

The prevalence of anemia among the elderly population was 38.4%. Increasing age, female sex, dependence in activities of daily living, and consumption of two or fewer meals per day were independently associated with anemia. These findings highlight the need for routine screening, nutritional assessment, and functional evaluation among the Indian elderly population. The study also underscores the importance of greater inclusion of the elderly population in national demographic and health surveys and consideration of this age group in future anemia prevention and control initiatives.

Key messages

Anemia in Indian elderly is an important health concern and will be a public health challenge in India due to demographic, epidemiological, and nutritional transition, leading to an increase in health care cost due to its complications. The present study on estimating burden of anemia may provide needed information for the policy makers to formulate nutritional policy and programs dedicated to improve the nutritional status of the elderly.

Conflicts of interest

There are no conflicts of interest.

SUPPLEMENTARY FILE 1

Pre-tested Semi-structured Questionnaire Used for Data Collection

Date:

Household No.

I. Socio-demographic characteristics

1. Age (years): _______

2. Sex

   □ Male

   □ Female

3. Religion

   □ Hindu

   □ Muslim

   □ Christian

   □ Others __________

4. Education

   □ Illiterate

   □ Just literate

   □ Primary

   □ Lower secondary

   □ Higher secondary

   □ PUC

   □ Graduate

   □ Postgraduate

5. Occupation

   □ Employed

   □ Unemployed

6. Marital status

   □ Married

   □ Unmarried

   □ Widowed

   □ Divorced

   □ Separated

II. Standard of Living Index (NFHS)

Item              Score

House type            ____

Source of lighting         ____

Toilet facility           ____

Main fuel for cooking       ____

Source of drinking water      ____

Separate room for cooking     ____

Ownership of house        ____

Ownership of agricultural land   ____

Ownership of irrigated land    ____

Ownership of livestock      ____

Ownership of durable goods    ____

Total Score (0–67):       __________

III. Dietary Assessment

1. Type of diet

   □ Vegetarian

   □ Mixed

2. Number of meals/day

   □ ≤2

   □ >2

3. Consumption of iron-rich vegetables

   □ ≥3 times/week

   □ <3 times/week

4. Consumption of iron-rich fruits

   □ Yes

   □ No

IV. Personal habits

1. Use of footwear

   □ Yes

   □ No

2. Alcohol consumption

   □ Yes

   □ No

V. Assessment of functional status: [Katz Activity of Daily Living (ADL)]:

Activities of Daily Living
Activities	Independence (1 Point)	Dependence (0 Points)
Points (1 or 0)	NO supervision, direction or personal assistance.	WITH supervision, direction, personal assistance or total care.
BATHING	(1 POINT) Bathes self completely or needs help in bathing only a single part of the body such as the back, genital area or disabled extremity.	(0 POINTS) Need help with bathing more than one part of the body, getting in or out of the tub or shower. Requires total bathing
Points:____
DRESSING	(1 POINT) Get clothes from closets and drawers and puts on clothes and outer garments complete with fasteners. May have help tying shoes.	(0 POINTS) Needs help with dressing self or needs to be completely dressed.
Points:
TOILETING	(1 POINT) Goes to toilet, gets on and off, arranges clothes, cleans genital area without help.	(0 POINTS) Needs help transferring to the toilet, cleaning self or uses bedpan or commode.
Points:____
TRANSFERRING	(1 POINT) Moves in and out of bed or chair unassisted. Mechanical transfer aids are acceptable	(0 POINTS) Needs help in moving from bed to chair or requires a complete transfer.
Points:____
CONTINENCE	(1 POINT) Exercises complete self control over urination and defecation.	(0 POINTS) Is partially or totally incontinent of bowel or bladder
Points:____
FEEDING	(1 POINT) Gets food from plate into mouth without help. Preparation of food may be done by another person.	(0 POINTS) Needs partial or total help with feeding or requires parenteral feeding.
Points:____
TOTAL POINTS: ____ □ Independent (Score 6) □ Dependent (Score ≤5)

VI. General Physical Examination

 Pallor

  □ Present

  □ Absent

VII. Haemoglobin Estimation

Capillary blood sample collected using finger prick.

Hb (g/dL): __________

VIII. Comorbidity Status

 Presence of physician-diagnosed chronic disease

  □ Yes

  □ No

 If Yes, specify (tick all that apply):

  □ Hypertension

  □ Diabetes mellitus

  □ Cardiovascular disease

  □ Chronic kidney disease

  □ Chronic respiratory disease

  □ Thyroid disorder

  □ Others: __________________

SUPPLEMENTARY FILE 2

STROBE Statement—Checklist of items that should be included in reports of cross-sectional studies

	Item No	Recommendation	Page No
Title and abstract	1	(a) Indicate the study’s design with a commonly used term in the title or the abstract	Page 1, Title; Page 2 (Abstract - Methods)
		(b) Provide in the abstract an informative and balanced summary of what was done and what was found	Page 2
Introduction
Background/rationale	2	Explain the scientific background and rationale for the investigation being reported	Page 3
Objectives	3	State specific objectives, including any prespecified hypotheses	Page 3
Methods
Study design	4	Present key elements of study design early in the paper	Page 4
Setting	5	Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection	Page 4
Participants	6	(a) Give the eligibility criteria, and the sources and methods of selection of participants	Pages 4–5
Variables	7	Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable	Pages 5–6
Data sources/ measurement	8*	For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group	Pages 5–6
Bias	9	Describe any efforts to address potential sources of bias	Pages 4–7
Study size	10	Explain how the study size was arrived at	Page 4
Quantitative variables	11	Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why	Pages 6–7
Statistical methods	12	(a) Describe all statistical methods, including those used to control for confounding	Pages 6–7
		(b) Describe any methods used to examine subgroups and interactions	Not applicable
		(c) Explain how missing data were addressed	Page 8 (participant flow/no missing baseline data)
		(d) If applicable, describe analytical methods taking account of sampling strategy	Pages 6–7
		(e) Describe any sensitivity analyses	Not applicable
Results
Participants	13*	(a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed	Page 8 (Figure 1)
		(b) Give reasons for non-participation at each stage	Page 8 (Figure 1)
		(c) Consider use of a flow diagram	Page 8 (Figure 1)
Descriptive data	14*	(a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential confounders	Pages 8–9; Table 1
		(b) Indicate number of participants with missing data for each variable of interest	None; all selected participants analysed (Page 8)
Outcome data	15*	Report numbers of outcome events or summary measures	Pages 8–10
Main results	16	(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence interval). Make clear which confounders were adjusted for and why they were included	Pages 9–10; Tables 1–3
		(b) Report category boundaries when continuous variables were categorized	Pages 5–6
		(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period	Not applicable
Other analyses	17	Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses	Page 10 (Model fit, ROC)
Discussion
Key results	18	Summarise key results with reference to study objectives	Pages 11–13
Limitations	19	Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias	Page 12
Interpretation	20	Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence	Pages 11–13
Generalisability	21	Discuss the generalisability (external validity) of the study results	Pages 11–13
Other information
Funding	22	Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based	Page 13

*Give information separately for exposed and unexposed groups. Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

Funding Statement

Nil.