|Year : 2022 | Volume
| Issue : 4 | Page : 362-369
Oral health related quality of life and its related factors among the elderly population in Davanagere city: A cross sectional survey
Puja C Yavagal1, Vajreshwari Narayanpur2, Sushmarani Rajanna2, BR Priyanka2
1 Professor, Department of Public Health Dentistry, Bapuji Dental College and Hospital, Davanagere, Karnataka, India
2 Intern, Bapuji Dental College and Hospital, Davanagere, Karnataka, India
|Date of Submission||02-Sep-2021|
|Date of Decision||17-Mar-2022|
|Date of Acceptance||29-Aug-2022|
|Date of Web Publication||19-Dec-2022|
Puja C Yavagal
Department of Public Health Dentistry, Bapuji Dental College and Hospital, Davangere, Karnataka
Source of Support: None, Conflict of Interest: None
Background: Identifying factors related to oral health-related quality of life (OHRQoL) helps to plan effective oral health programs among elderly population. Aim: The aim was to assess OHRQoL and factors related to it among elderly population in Davanagere city. Materials and Methods: This cross-sectional survey involved a multistage stratified sample of 380 elderly population aged 60 years and above in Davanagere city. Data related to demographic details and general and oral health-related factors, nutritional status, and geriatric OHRQoL of study participants were recorded using a study pro forma, Mini Nutritional Assessment-Short Form Index, and Geriatric Oral Health Assessment Index (GOHAI), respectively. IBM SPSS Statistics for Windows, Version 21 (IBM Corp., Armonk, N.Y., USA) was used for statistical analysis. The significant level was fixed at P < 0.05. Chi-square test, Pearson's and Spearman's correlation tests, and multiple linear regression analysis tests were applied for data analysis. Results: The participants had good OHRQoL (mean GOHAI = 40.66 ± 7.29). The GOHAI was significantly (P < 0.05) negatively correlated with age, systemic problems, number of missing teeth, source of income, and medications and was positively correlated with nutritional status, occupation, and pan chewing. Age, medical problems, missing teeth, nutritional status, and occupation were significant predictors of GOHAI. (F = 26.36, P < 0.00, r2 = 0.36, B [Constant] =48.65 [confidence interval 33.85–63.46]). Conclusion: Clinicians, public health workers, and policy makers can focus on significant predictors of GOHAI for planning community-based programs targeted at improving OHRQoL of elderly population.
Keywords: Correlation, geriatric dentistry, Geriatric Oral Health Assessment index, predictors
|How to cite this article:|
Yavagal PC, Narayanpur V, Rajanna S, Priyanka B R. Oral health related quality of life and its related factors among the elderly population in Davanagere city: A cross sectional survey. J Indian Assoc Public Health Dent 2022;20:362-9
|How to cite this URL:|
Yavagal PC, Narayanpur V, Rajanna S, Priyanka B R. Oral health related quality of life and its related factors among the elderly population in Davanagere city: A cross sectional survey. J Indian Assoc Public Health Dent [serial online] 2022 [cited 2023 Feb 9];20:362-9. Available from: https://www.jiaphd.org/text.asp?2022/20/4/362/364017
| Introduction|| |
Geriatric health care is a major public health concern in India, because there are around 100 million elderly at present, and the number is expected to increase to 323 million constituting 20% of the total population by 2050. The rate of growth of elderly population of India is much faster than total population. Many elders face health challenges such as degenerative, physical, mental, and cognitive diseases and are at constant risk of noncommunicable, cerebral, and cardiovascular diseases and communicable diseases. Oral health challenges such as tooth loss, dental caries, periodontal disease, xerostomia, and oral precancer/cancer lesions have negative impact on daily life. Many studies have pointed out the association between oral health problems and the risk of malnutrition as a result of reduced chewing ability and processing of foods, tooth loss, lack of or inadequate prosthetic rehabilitation, and the presence of pain or discomfort related to caries or tooth fractures.
The impact of oral health on quality of life broadens the research boundaries beyond just clinical indicators. Hence, with clinical indicators, use of oral health-related quality of life (OHRQoL) instruments might help appraise the oral health problems in elderly population in an effective manner. The Geriatric Oral Health Assessment Index (GOHAI), developed by Atchison and Dolan in 1990 is a well-established OHRQL instrument for use among the elderly population. Its internal consistency is satisfactory, and its validity has been confirmed. The 12-item GOHAI evaluates three dimensions of OHRQL, which includes physical function, psychosocial function, and pain or discomfort.
Several factors such as demographic, socioeconomic, self-perceived oral health, prosthetic status, systemic health, and nutritional level have been linked with OHRQL. Studies that identify factors associated with OHRQoL among elderly population can contribute to the formulation of more effective oral health programs directed at the population and help prioritize the allocation of limited financial resources. For effective planning of preventive, promotive, curative, and rehabilitative oral health care services for elderly population, dentists need to consider the factors related to OHRQoL. Literature search revealed very few Indian studies which have tried to explore factors related to OHRQoL among elderly population and identify the possible predictors of OHRQoL. Hence, a cross-sectional survey was planned to assess OHRQoL and its related factors among elderly population in Davanagere city, India. Research question framed was “What is the level of OHRQoL of elderly population in Davanagere city, India? and what are the factors and predictors related to it?”
| Materials and Methods|| |
An observational, descriptive, cross-sectional questionnaire survey was done to assess OHRQoL and its related factors and predictors among elderly population in Davanagere city, India. The synopsis of the proposed study was approved by the Institutional Review Board (BDC/Exam/509/2019–2020); sample size was calculated using an online sample size calculator. Formula for calculating the sample size was based on finite population characteristics, n = (z2P [1 − P]/e2)/1+ (z2P [1 − P]/e2N); where, n = sample size, N = population size (total population of elderly people aged 60 years and above in urban area of Davanagere = 14,622), Z = z score at 95% confidence level (1.96), e = margin of error (5%). Substituting the above-mentioned values, sample size was estimated to be 375, which was rounded off to 380. Multistage stratified random sampling technique was used to select the study participants. Davanagere city was arbitrarily divided into four zones such as North East, North West, South East, and South West for sampling purpose. From each zone, two corporation wards were selected randomly. From each ward, two localities were randomly selected. From each locality, a street was randomly selected, and from each selected street, a door-to-door survey was conducted to select the participants based on selection criteria. From two wards, 95 participants were selected. In a similar manner, participants were recruited from other three zones making a total of 380 participants. A sampling frame was prepared, and lottery method was employed to select sample randomly from the frame at every stage of sampling.
Inclusion criteria for participants were as follows: elderly people in Davanagere city belonging to the age group of 60 years and above, elderly people residing in their homes as well as in old-age homes of Davanagere city, participants who gave voluntary informed written consent for participation, participants who were willing to share their personal details for the study, and who were present at the site during the study. Exclusion criteria were as follows: participants who had mental disorders affecting communication and memory function were excluded (e.g. Alzheimer's disease), and who had difficulty in answering questions due to any physical and psychological problems. Voluntary written informed consent was obtained from the study participants after explaining them about the purpose of conducting the study and procedure of collecting the data through participant information form. Data were collected using a self-designed structured pro forma containing both open- and closed-ended questions.
Proforma sections to record sociodemographic details, history of medical conditions and medications, nutritional status, GOHAI, and oral health data.
Methodology for collection of data
The study was carried out in field setting. The study duration lasted for 5 months, from October 2019 to March 2020. Information regarding sociodemographic characteristics, medical condition, medications, and habits was collected by investigator by asking questions to the participants, followed by assessment of nutritional status of the participants, and recording their oral health data by Type III oral examination. Both the investigator and assistant were trained and calibrated by a senior professor to administer Mini Nutritional Assessment-Short Form (MNA-SF) and GOHAI questionnaires in the Department of Public Health Dentistry. Training and calibration procedure included administering of all the questionnaires in pro forma and recording the responses of 20 elderly participants, separately by professor, investigator, and assistant. The discrepancy in recording was noted, and interexaminer reliability (Kappa score) was noted to be 0.85 and 0.80 for MNA-SF and GOHAI, respectively, which reflected a good interexaminer reliability. The same 20 participants were recalled at different time periods, and the assistant rerecorded the findings. The intraexaminer reliability was calculated to be k = 0.80, 0.82 for MNA-SF and GOHAI, respectively, which reflected a good intraexaminer reliability.
Assessment of nutritional status
Nutritional status was recorded using MNA-SF Index, which was translated to local language (Kannada). It was a 6-item questionnaire, which was a widely used and validated questionnaire, to assess the nutritional status of elderly population in surveys., Specific scores were assigned to responses, and the total score ranged from 0 to 14. Scores were interpreted as follows: 0–7: malnourished, 8–11: at risk of malnutrition, and 12–14: normal nutritional status. Body mass index was calculated by measuring the height and weight of the participants using a measuring tape and calibrated, digital portable weighing machine.
Collection of data regarding missing teeth and prosthetic status
Oral examination of study participants using mouth mirror and CPI probe was done to record data regarding number of missing teeth and prosthetic status by an investigator under strict aseptic precautions using autoclaved instruments.
Data regarding oral health-related quality of life
OHRQoL was recorded using GOHAI. It is a validated index widely used to record OHRQoL among elderly population. It consists of 12 items which assess the dimensions of physical functions, psychosocial functions, and pain or discomfort for the past 3 months. Responses were rated on a 5-point Likert scale interpreted as: always = 1, often = 2, sometimes = 3, seldom = 4, and never = 5. The total GOHAI score was calculated by summing of all the scores of questions, and the final GOHAI score was in range from 12 to 60.
Language validity of the questionnaire was established, as the questionnaire was translated to local language (Kannada) by back translation method. The validity of Hindi (national language) version of GOHAI has already been established for Indian population.
A pilot study was conducted to check the feasibility, reliability, and internal consistency of self-designed pro forma. The Kappa score was 0.9, which reflected a good reliability of the questionnaire. Cronbach's alpha was 0.75, which reflected a good internal consistency. The questionnaire was assistant administered.
IBM SPSS Statistics for Windows, Version 21 (IBM Corp., Armonk, N.Y., USA) was used for analysis of data. The significant level was fixed at P < 0.05. Kolmogorov–Smirnov test was used to check normality of data. Descriptive statistics was generated in terms of frequencies or percentages. Pearson's and Spearman's correlation tests were used to assess correlation between the variables. Multiple linear regression analysis was performed to assess predictor variables related to OHRQoL.
| Results|| |
Data were dichotomized for sex, age, marital status, socioeconomic status, systemic problems, medications use, smoking status, alcohol consumption, and pan-chewing habit. Data were assessed on ordinal scale for nutritional status. Variables such as age, geriatric OHRQoL, nutritional status, and missing teeth were on continuous scale. Observations from [Table 1] depict sociodemographic and health profile of study population. Majority of study participants were female (58.7%), aged 60–75 years (67%), married (77.9%), unemployed (59.7%), and belonged to middle-class socioeconomic status (73.3%) according to modified Kuppuswamy classification for socioeconomic status (2019). Around 82.2% of them were financially dependent on their children and lived with them (72.6%). Around 82.9% of them were educated. Many participants had systemic problems (71.1%) and were on medications (70.5%). They were engaged in habits such as smoking (77.1%), alcohol consumption (16.8%), and pan chewing (45.8%). The mean MNA-SF score of study population was 11.27 ± 1.56, suggesting borderline risk of malnutrition. The mean number of missing teeth among study sample was 10.92 ± 9.94. Around 80% were partially edentulous, and 13.2% were completely edentulous. In spite of this, only 49.2% had prosthesis.
|Table 1: Sociodemographic and health-related factors of study population|
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[Table 2] shows that the mean GOHAI score of study participants was 40.66 ± 7.29, which suggested a good OHRQoL among them. The median GOHAI scores of study population were high among males (43), employed (43), middle class (43), pensioned (43) living with children (43), illiterates (48), who had no systemic problems (44.5), who were not on medications (44), nonsmokers (43), alcoholics (43.5), removable prosthesis wearers (43), and completely edentulous (45). Data related to GOHAI, missing teeth, and nutritional status (MNA scores) did not follow normal distribution (P < 0.05) according to Kolmogorov–Smirnov test; hence, nonparametric tests were considered for data analysis. When GOHAI scores were compared across different groups of demographic variables, GOHAI scores were significantly higher (P < 0.05) among employed, pensioned, systemically healthy, who were not on any medication, and pan chewers. [Table 3] indicates that GOHAI scores were significantly (P < 0.05) negatively correlated with age (r = −0.21), missing teeth (r = −0.18), source of income (r = −0.11), medical problems (−0.22), and medications (r = −0.15), and were significantly (P < 0.05) positively correlated with nutritional status (0.44), pan chewing (r = 0.14), and occupation (r = 0.14). Observations of [Table 4] show results of multiple linear regression analysis. A multiple linear regression was done to predict GOHAI scores based on predictor variables: occupation, missing teeth, source of income, age, nutritional scores, medications, pan chewing, and medical problems. A significant regression equation was found (F = 26.36, P < 0.00), with an r2 of 0.36. Participants' predicted GOHAI was 48.65 which was statistically significant (p=0.00, CI: 33.85-63.46). Coding of variables was as follows, occupation:1-unemployed, 2-employed; missing teeth: number of missing teeth per person; source of income: 1-pensioned, 2-dependent on child; age: years, nutritional status: total MNA SF score; medications: 1-not on medications, 2-on medications; pan chewing: 1-non pan chewers, 2-pan chewers; medical problems: 1-no medical problems, 2-suffering from medical problems. Number of missing teeth, gender, nutritional status, age, medical problems, and occupation were significant predictors (P < 0.05) of GOHAI.
|Table 2: Comparison of Geriatric Oral Health Assessment Index scores across different variables of study population|
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|Table 3: Correlation of Geriatric Oral Health Assessment Index scores with different demographic and clinical variables|
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| Discussion|| |
The present cross-sectional survey was conducted among 380 elderly population with the mean age of 63.13 ± 3.18 years to assess their OHRQoL and its related factors and predictors. The mean GOHAI score of study population was 40.66 ± 7.29, which indicated a good OHRQoL among the study participants. Similar results were observed in few Indian studies.,, However, contradictory findings were seen in few Indian studies where GOHAI was poor among study population., The results of the study indicated that OHRQoL was positively correlated with nutritional status and employment status and negatively correlated to age, systemic problems, missing teeth, source of income, and medications. Similar results were observed in few populations., Perhaps, people with a good nutritional status experience a fewer dental problems and have improved quality of life, and people who are employed can afford health-care services and seek necessary treatment for their dental problems and exhibit improved OHRQoL. In the present study, pan chewers exhibited a good GOHAI. The reason for this could be the high prevalence of pan chewers in the present study. Similar finding was seen in a study. However, contradicting finding was observed, where GOHAI was negatively correlated with pan chewing. Aging is associated with increased prevalence of systemic diseases and physical and physiological deficiencies, which are linked to increased prevalence of oral diseases like missing teeth leading to poor OHRQoL. However, contradictory to this finding, GOHAI was positively correlated with age and missing teeth. In the present study, multiple linear regression analysis was done to identify the strong predictor variables of GOHAI. Since the GOHAI score was a continuous variable, linear regression model was chosen. The assumptions of collinearity and homoscedasticity were checked before analyzing the results of regression. Even though the r2 (goodness of fit) was <50%, the results of the test cannot be ignored. Furthermore, GOHAI was significantly related to predictor variables in the regression model as depicted by ANOVA test in regression model summary. Age, number of missing teeth, nutritional status, medical problems, nutritional status, and medical problems were significant predictors of GOHAI. In similar studies conducted to assess the various predictors of GOHAI, it was observed that missing teeth, age, medical problems, and nutritional status were predictors of GOHAI.
As age increased, the OHRQoL decreased in the present study participants. Perhaps, the reason for this could be more oral health-related problems associated with increasing age due to loss of teeth, associated systemic problems, side effects related to medications like xerostomia, and poor oral hygiene maintenance related to decreased manual dexterity. Since with aging there is increased tooth loss and decreased ability to perform masticatory function, this can lead to poor OHRQoL. Similar findings were seen in few studies., However, contradictory results were observed in studies done by Alshammari et al. and Zhao et al.
Systemic problems and geriatric OHRQoL: In the present study, people with systemic problems have low GOHAI scores. Since systemic diseases are strongly linked with high prevalence of oral health problems in elderly such as xerostomia, periodontal disease, stomatitis, nonhealing ulcers, loss of teeth leading to loss of masticatory function, and speech difficulties, the OHRQoL is poor. Similar results were seen in study done by Nosratzehi et al., in 2019. Missing teeth and geriatric OHRQoL: Increase in number of missing teeth resulted in poor GOHAI in the present study. This could be due to functional and psychological limitations associated with loss of masticatory function and impaired esthetics, respectively. Similar findings were observed in few studies., However, a study reported contradictory finding to this. Nutrition and GOHAI: In the present study, people with improved nutritional status had better GOHAI compared to those who were at risk of malnutrition. Impaired nutritional status is linked with various oral health-related problems such as xerostomia, stomatitis, nonhealing ulcers, periodontitis, and tooth loss leading to poor OHRQoL. Similar findings were seen in few studies., However, in few studies, no significant correlation was seen between MNA scores and GOHAI., In the present study, MNA-SF index was used to assess the nutritional status. The MNA tool is an internationally well-accepted, validated tool for assessing malnutrition in the elderly and it fulfills many criteria for both screening and diagnostic measures. The MNA® is a short, valid nutritional screening tool for free-living and clinically relevant elderly populations. The MNA contains geriatric-specific assessment questions related to nutritional and health conditions, independence, quality of life, cognition, mobility, and subjective health. Rubenstein et al. developed a 6-question MNA-SF by identifying a subset of questions from the full MNA that had high sensitivity, specificity, and correlation to the full MNA. This MNA-SF identifies elderly individuals as well-nourished or at risk of malnutrition so that the full MNA is needed only if a patient is classified as at risk. The diagnostic accuracy of this original MNA-SF in identifying the elderly as well-nourished is comparable to the full MNA, and it is a valid time saving alternative.
The study was first of its kind done among the present geographic population, which would provide a baseline data for further studies. Majority of demographic and clinical variables were tested in regression analysis. Oral hygiene practices of the study participants were not assessed, which could have been a strong predictor of OHRQoL. Questionnaire was investigator-administered, since few elderly people were illiterates; hence, social desirability bias could have influenced the results of the study.
The study provides a strong baseline data, which could be helpful in planning oral health programs and services to the elderly population. These data may also be helpful for further research involving elderly population in that area. The study underlines the potential predictors of OHRQoL among elderly, which may be considered by clinicians while planning treatment for such population as well as program planners and policy makers for planning of oral health programs for such population.
| Conclusion|| |
The study participants had a good OHRQoL and a normal nutritional level. Number of missing teeth, nutritional status, age, medical problems, and employment status were significant predictors of GOHAI.
The authors would like to acknowledge the support of all the final year undergraduate dental students (Batch 2021) of Bapuji Dental College and Hospital and the participants of the study.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Jeyaseelan M, Prabu G. Family, and marginalization of elders. Indian J Appl Res 2014;4:601-3.
Patel P, Shivakumar KM, Patil S, Suresh KV, Kadashetti V. Association of oral health-related quality of life and nutritional status among elderly population of Satara district, Western Maharashtra, India. J Indian Assoc Public Health Dent 2015;13:269. [Full text]
Allen PF. Association between diet, social resources and oral health related quality of life in edentulous patients. J Oral Rehabil 2005;32:623-8.
Atchison KA, Dolan TA. Development of the geriatric oral health assessment index. J Dent Educ 1990;54:680-7.
Government of India, Ministry of Home affairs. Census of India 2011. Our Census, Our Future. Available from: http://censusindia.gov.in
. [Last accessed on 2020 Jul 25].
Bauer JM, Kaiser MJ, Anthony P, Guigoz Y, Sieber CC. The Mini Nutritional Assessment – Its history, today's practice, and future perspectives. Nutr Clin Pract 2008;23:388-96.
Kaiser MJ, Bauer JM, Ramsch C, Uter W, Guigoz Y, Cederholm T, et al.
Validation of the Mini Nutritional Assessment short-form (MNA-SF): A practical tool for identification of nutritional status. J Nutr Health Aging 2009;13:782-8.
Jain R, Dupare R, Chitguppi R, Basavaraj P. Assessment of validity and reliability of Hindi version of geriatric oral health assessment index (GOHAI) in Indian population. Indian J Public Health 2015;59:272-8.
] [Full text]
Saleem SM. Modified Kuppuswamy socioeconomic scale updated for the year 2019. Indian J Forensic Community Med 2019;6:1-3.
Kshetrimayum N, Reddy CV, Siddhana S, Manjunath M, Rudraswamy S, Sulavai S. Oral health-related quality of life and nutritional status of institutionalized elderly population aged 60 years and above in Mysore City, India. Gerodontology 2013;30:119-25.
Rekhi A, Marya CM, Nagpal R, Oberoi SS. Assessment of oral health related quality of life among the institutionalised elderly in Delhi, India. Oral Health Prev Dent 2018;16:59-66.
Kundapur V, Hegde R, Shetty M, Mankar S, Hilal M, Prasad A H. Effect of loss of teeth and its association with general quality of life using geriatric oral health assessment index (Gohai) among older individuals residing in rural areas. Int J Biomed Sci 2017;13:6-12.
Shivakumar KM, Patil S, Kadashetti V, Raje V. Oral heal-related quality of life of institutionalized elderly in Satara District, India. J Datta Meghe Inst Med Sci Univ 2018;13:183-9. [Full text]
Chahar P, Mohanty VR, Aswini YB. Oral health-related quality of life among elderly patients visiting special clinics in public hospitals in Delhi, India: A cross-sectional study. Indian J Public Health 2019;63:15-20.
] [Full text]
Raja BK, Radha G. Self-perceived oral function and factors influencing oral health of elderly residents in Bengaluru city, India. J Health Res Rev 2015;2:29. [Full text]
Zhao L, Lin HC, Lo EC, Wong MC. Clinical and socio-demographic factors influencing the oral health-related quality of life of Chinese elders. Community Dent Health 2011;28:206-10.
Shao R, Hu T, Zhong YS, Li X, Gao YB, Wang YF, et al.
Socio-demographic factors, dental status and health-related behaviors associated with geriatric oral health-related quality of life in Southwestern China. Health Qual Life Outcomes 2018;16:98.
Rosli TI, Chan YM, Kadir RA, Hamid TA. Association between oral health-related quality of life and nutritional status among older adults in district of Kuala Pilah, Malaysia. BMC Public Health 2019;19:547.
Venkatesan A, Annie Sylvea V, Ramalingam S, Seenivasan MK, Narasimhan M. Evaluation of oral health status using the geriatric oral health assessment index among the geriatric population in India: A pilot study. Cureus 2020;12:e7344.
A-Dan W, Jun-Qi L. Factors associated with the oral health-related quality of life in elderly persons in dental clinic: Validation of a Mandarin Chinese version of GOHAI. Gerodontology 2011;28:184-91.
Alshammari M, Baseer MA, Ingle NA, Assery MK, Al Khadhari MA. Oral health-related quality of life among elderly people with edentulous Jaws in Hafar Al-Batin Region, Saudi Arabia. J Int Soc Prev Community Dent 2018;8:495-502.
Nosratzehi T, Nosratzehi S, Nosratzehi M, Ghaleb I. Oral health-related quality of life in patients with rheumatoid arthritis. Open Access Rheumatol 2019;11:309-13.
Santucci D, Attard N. The oral health-related quality of life in state institutionalized older adults in Malta. Int J Prosthodont 2015;28:402-11.
Banerjee R, Chahande J, Banerjee S, Radke U. Evaluation of relationship between nutritional status and oral health related quality of life in complete denture wearers. Indian J Dent Res 2018;29:562-7.
] [Full text]
Rahman KM, Khalequzzaman M, Khan FA, Rayna SE, Samin S, Hasan M, et al.
Factors associated with the nutritional status of the older population in a selected area of Dhaka, Bangladesh. BMC Geriatr 2021;21:161.
Pillai RS, Mathur VP, Jain V, Shah N, Kalra S, Kumar P, et al.
Association between dental prosthesis need, nutritional status and quality of life of elderly subjects. Qual Life Res 2015;24:2863-71.
Ghosh A, Dasgupta A, Paul B, Sembiah S, Biswas B, Mallik N. Screening for malnutrition among elderly with MNA Scale: A clinic based study in rural area of West Bengal. IJCMR 2017;4:1978-82.
Rubenstein LZ, Harker JO, Salvà A, Guigoz Y, Vellas B. Screening for undernutrition in geriatric practice: developing the short-form mini-nutritional assessment (MNA-SF). J Gerontol A Biol Sci Med Sci 2001;56:M366-72.
[Table 1], [Table 2], [Table 3], [Table 4]