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Journal of Human Nutrition & Food Science

Cite this article: Boulos C, Yaghi N, Salameh P (2017) Impact of Education and Other Potential Covariates on Nutritional Status in Lebanese Rural Elderly Women. J Hum Nutr Food Sci 5(3): 1113.

*Corresponding authorChrista Boulos, Department of Nutrition, Faculty of Pharmacy, St Joseph University, Beirut, Damascus Street, Lebanon, Tel: 00961 3 449532; Email: christa.

Submitted: 02 August 2017

Accepted: 31 August 2017

Published: 02 September 2017

ISSN: 2333-6706

Copyright© 2017 Boulos et al.

OPEN ACCESS

Research Article

Impact of Education and Other Potential Covariates on Nutritional Status in Lebanese Rural Elderly WomenBoulos Christa1*, Yaghi Nathalie1, and Salameh Pascale2

1Department of Nutrition, Saint Joseph University, Lebanon2Department of Pharmacy, Lebanese University, Lebanon

Abstract

Background: In Lebanon, an Arab Mediterranean country, nutritional status of elderly population was described as pooled results from both genders, underestimating the influence of education on nutritional status in Lebanese rural elderly women.

Objective: We aimed to study the impact of education and other covariates on nutritional status in Lebanese elderly women.

Materials and Methods: This cross-sectional study included a representative sample of 607 community dwelling elderly women from all rural districts in Lebanon. Data included socio demographic factors, health related characteristics [comorbidities, chronic pain, ADL disabilities etc.], mental health status [5 item GDS, MMS], social isolation [LSNS 6 scale] and variables related to food intake. Nutritional status was assessed through the Mini Nutritional Assessment.

Results: After identifying significant associated factors in the bivariate analysis, a stepwise multiple logistic regression found a strong and independent relationship between the different levels of education versus illiteracy, and nutritional status [OR: 0.58; 95% CI: 0.35-0.96 and OR: 0.42; 95% CI: 0.21-0.83]. Social isolation, low physical health status, depression, cognitive dysfunction were other significantly associated covariates. Furthermore, women participating in meal preparation were at lower risk of poor nutritional status [OR: 0.30; 95% CI: 0.18-0.50].

Conclusion: We demonstrated that educational status in women was a strong predictor of poor nutritional status in this representative sample of rural elderly women. We should therefore emphasize on the importance of access to education and information in particular in female population living in rural areas.

Keywords•Malnutrition; Nutritional status; Female; Elderly;

Education

ABBREVIATIONSAMEL: Aging and Malnutrition in Elderly Lebanese; MNA: Mini

Nutritional Assessment; SRH: Self-Related Health; MMS: Mini Mental Status; GDS: Geriatric Depression Scale; ADL: Activities of Daily Living; LSNS: Lubben’s Social Network Scale; BMI: Body Mass Index; SPSS: Statistical Package for Social Sciences; ORa: Adjusted Odds Ratio; CI: Confidence Interval

INTRODUCTIONLebanon is experiencing a rapid demographic transition, like

most Mediterranean Arab countries. People aged 65 years and over are estimated to represent around 10 % of the population by 2025 [1]. Lebanon is one of the 9 countries in the Middle East [Algeria, Bahrain, Kuwait, Libya, Morocco, Oman, Qatar and Tunisia] that is expected to have more older persons than children [under 15 years old] by 2050 [2,3].

In Lebanon, a wide range of studies have emphasized the importance of socio-economic factors for successful aging and decreased mortality among the elderly population [4,5]. As in all Middle Eastern patriarchal and deeply traditional societies,

women suffer from gender-based social and health inequities. Traditionally, in the Arab world, women’s role is mainly restricted as the major care provider and a pillar of the support system within the family; they provide most formal and informal care [2]. Nevertheless, women’s position in society varies between Middle Eastern countries; low female education and employment rates are commonly encountered, and are mainly due to traditional social models where women are considered as mothers and wives rather than as individuals with their own needs and goals. It is men’s responsibility to be the sole breadwinner and women have restricted behavior and mobility. Older women in particular are faced with many challenges regarding social exclusion, financial capital, and geographical segregation [1,4].

Gender gaps in education have been shown to have a significant negative influence on female health outcomes, and despite substantial efforts made over the last few decades in the region to address these disparities, significant numbers of women remain illiterate or are excluded from the educational process [4,6]. The level of education a woman receives is not only a key source of knowledge and information but also an important predictor of female life and determinant of health outcomes [7].

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On the other hand, nutrition is an important element of health and well-being among the elderly [8]. Poor nutritional status is related to reduced functional status, immune dysfunction, impaired cognitive function, poor wound healing, higher hospital readmission rates, and increased mortality [2,9]. Several factors affect accessibility and availability of nutrients during a woman’s life particularly during aging. The nutrition and health care needs of elderly women are affected by the multiple life circumstances, such as income, living situation, education, and health status [10].

So far, malnutrition and associated factors have been reported as pooled results in both genders [11,12], while gender-specific factors related to nutritional status, particularly in the Middle East, are sparse. Thus the purpose of the present study was to provide information about the impact of education and other covariates on nutritional status in Lebanese elderly women.

MATERIALS AND METHODSThe study sample of 607 women was drawn from the AMEL

[Aging and Malnutrition in Elderly Lebanese] study, a national survey including 1200 randomly selected Lebanese elderly aged 65 years and above. The survey was conducted between March 2011 and March 2012 and included community dwelling elderly living in the 24 rural districts [Caza] all over the country. Participants were interviewed by trained fieldworkers in their homes after oral consent. In case the person was unable to respond, help from a proxy respondent was required. The inclusion criteria were: being at least 65 years old at the time of the data collection, living at home in rural districts of Lebanon, being free from terminal illness and not tube fed. The participation rate was 95.3%. Details of the sampling procedure of the AMEL study have been published in a previous article [6].

Ethical consent was obtained from the research council of the St Joseph’s University.

A multi component questionnaire was used, translated back and forth from French to Arabic by two persons fluent in both languages. This questionnaire included the following measures:

Nutritional status

The Arabic version of the Mini Nutritional Assessment [MNA] was used to evaluate the nutritional status of the participants. The MNA is composed of 18 questions related to anthropometric, general, dietetic, and subjective assessment [13]. It is the most widespread nutritional assessment tool and has been validated in various settings. The total score ranges between 0 and 30 points. A score less than 17 indicates malnutrition, between 17 and 23.5 is considered as risk for malnutrition, whereas a score ≥ 24 indicates adequate nutritional status. In this study we defined “poor nutritional status” as being either malnourished or at risk of malnutrition.

Sociodemographic status

For assessment of sociodemographic status, following data were collected: age, marital status, educational status [illiterate, primary school, middle school, high school graduation or university level]. Average monthly income was categorized into: <300.000 LL [200 USD], 300.000 – 600.000 LL and >600.000 LL [400 USD] according to the national minimum wages.

Furthermore participants were asked whether they live alone or with other people.

Social isolation was measured through the Lubben Social Network Scale 6 [LSNS 6], a short form derived from the original LSNS scale [14,15]. This scale was especially developed for older adults living in the community and was associated with several physical and mental health outcomes in previous studies [14]. The LSNS 6 is based on 3 questions assessing the family network, as follows:”How many relatives do you see or hear from at least once a month? How many relatives do you feel close to such that you could call on them for help? How many relatives do you feel at ease with that you can talk to about private matters?” These same questions were repeated by replacing the word “relatives” with the word “friends”. The answers were as follows: none [coded 0], one [coded 1], two [coded 2] three or four [coded 3], five to eight [coded 4], nine or more [coded 5]. The total score is the sum of the 6 items, ranging from 0 to 30. According to the author [14], at a score below 12, the person is considered as at risk for social isolation.

Health related condition

Physical health status was investigated through SRH [self-related health], a 5 item scale and the number of comorbidities diagnosed by a physician. Furthermore chronic pain was evaluated, as this condition was significantly associated with nutritional status in the previous study [11]. Subjective oral health was analyzed by questioning the elderly person about the presence of chewing problems which may influence food intake.

Cognitive functioning was assessed by the Mini-mental state [MMS] examination [19]. Due to the high number of illiterate elderly, we developed a modified version adapted to illiterate subjects [MMS 2], and assigned the original MMS [MMS1] translated in Arabic language to the literate elderly. As no cut-off points were defined in Lebanese elderly, the results were divided into quartiles in multivariate analysis. Thus, individuals were classified into four categories with the 1st quartile being considered as the lowest level of cognition. Mood was assessed through the 5 item Geriatric Depression Scale [GDS-5], a five item scale, recently validated among Lebanese elderly [16] and derived from the 15 item GDS Scale. This scale allows detecting depressive disorders in older adults at a score of two or more out of five [20].

Functional status

Functional status was assessed based on the Arabic version of the ADL [Activities of Daily Living] scale validated previously among Lebanese elderly [17]. This scale includes basic activities such as: bathing, toileting, and clothing, walking and eating by his/her own. Continence was excluded from this scale because difficulties in bladder or bowel control reflect an abnormality in a particular physical system and should therefore be considered as impairment rather than a disability [18]. We defined two categories: being totally independent for all five ADL and being dependent for at least one ADL.

BMI and food related characteristics

Weight of the participants was measured to the nearest 0.1 kg in light indoor clothes without shoes using an electronic

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digital scale. Height was measured in standing position to the nearest 0.5 cm. Then Body Mass Index [BMI] was computed as the ratio of weight [kilograms] to the square of height [meters]. BMI categories were defined as followed: <21 [underweight], 21-24.9 [normal weight], 25-29.9 [overweight], ≥ 30 [obesity]. Furthermore, participants were asked if they were following a special restrictive diet [low sodium diet, diabetic diet, and diet low in cholesterol/fat]. As a low sodium diet is frequently prescribed and considered to be the most anorexigenic, it has been entered as a single category [19]. In addition, people were asked if they were sharing meals with others. Answers were dichotomized into: more often [most of the time or half the time] and less often [infrequently or never]. Finally, preparing meals and cooking [or participating in cooking] was categorized into two groups: yes or no/rarely.

Statistical analysis

The Statistical Package for Social Sciences [SPSS] version 19.0 was used for data analysis. Chi-square tests were applied to assess bivariate associations between the different categories of explanatory variables [socio-demographic indicators, physical and mental health characteristics etc.] and nutritional status. ANOVA test was used to compare means across classes of nutritional status for continuous variables [age, MMS 1/2]. In order to identify independent variables associated with poor nutritional status several models of multivariate logistic regression analysis were performed after introducing the main explanatory variables that were associated with nutritional status at p ≤ 0.05 in the bivariate analysis. Odds-ratios with 95% confidence intervals were calculated. The dependent variable was nutritional status dichotomized as poor [malnourished or at risk] versus normal nutritional status. Regarding the variable “educational status”, the categories “middle school/high school and university level” were merged into one single category because of few individuals with higher educational level. In model 1, all socio-demographic determinants of nutritional status were introduced simultaneously. Then, in model 2, we entered the main health related characteristics adjusted for those socio-demographic variables which were significantly associated to poor nutritional status in model 1. In model 3 we entered the BMI as a continuous variable and the variables related to food intake which was adjusted for those significantly associated with nutritional status in model 2. A final model [model 4] was run with all the significantly associated variables in the three previous models. In all models, age was forced.

RESULTSThe study population included 607 women with a mean age

of 75 years [63-96 years]. The prevalence of malnutrition and risk of malnutrition among these female participants was 9.1% and 35.3%, respectively [results not shown].

Sociodemographic characteristics and nutritional status

Table 1 presents the sociodemographic characteristics of the participants distributed by nutritional status following bivariate analysis. Elderly with poor nutritional status [malnourished/at risk of malnutrition] were significantly older compared to

those who had a normal nutritional status [p< 0.001]. Moreover, nutritional status differed following geographical region: in fact, prevalence of malnutrition was higher among women living in the valley of the Bekaa and in South Lebanon as compared with those living in other parts of the country. Also, a strong and significant relationship was found between poor nutritional status and both, being widowed and having a low educational status [p< 0.001]. In addition, more that 50% of those suffering from social isolation [n=291] presented either malnutrition or risk of malnutrition [p<0.001]. However, living alone was not associated with poor nutritional status.

Health condition and nutritional status

The association between nutritional status and health-related condition is displayed in table 2. Malnutrition and risk of malnutrition were significantly more frequent in women suffering from worse physical health condition such as high level of comorbidities, lower SRH, chronic pain and disability [<0.001]. Furthermore nutritional status was significantly associated with depression; in fact 64.2% of the individuals with depressive mood were either malnourished or at risk of malnutrition [p<0.001]. As for cognitive status, mean MMS1 and MMS2 scores were both significantly lower among elderly women suffering from impaired nutritional status [p<0.001].

BMI, food related variables and nutritional status

When studying the association between malnutrition and some characteristics related to food preparation and intake (table 3), we found that following a restrictive diet and following a low salt diet, were both significantly related to poor nutritional status. Also and as expected, a low BMI was significantly associated with malnutrition [p<0.001]. Furthermore, elderly sharing meals with others and those participating in cooking were significantly less often malnourished.

Factors independently associated with poor nutritional status

Table 4 shows the results of multivariate logistic regression analysis with poor nutritional status as outcome variable. We introduced in this models the main significantly associated variables with poor nutritional status following bivariate analysis. In model 1, after entering sociodemographic factors, no association was found between age and nutritional status. Regarding educational status, we found a strong and independent relationship between a higher level of education and a reduced risk of poor nutritional status [p<0.001]; in fact, when compared to the category of illiterate female, the highest category of education was associated with a 67% reduced risk of poor nutritional status. Furthermore, being widowed and being socially isolated were both strongly associated with an increased risk of poor nutritional status [ORa: 1.98; 95% CI: 1.34-2.92 and ORa: 2.82; 95% CI: 1.93-4.14, respectively].

In model 2, all introduced health related variables [more than 3 comorbidities, chronic pain, disability, depression, and lower cognitive function] showed significant relationships with nutritional status. For example, depressive disorders were associated with a 3.5 fold increased risk of poor nutritional status [p<0.001]. Furthermore, the three sociodemographic variables [education, widowhood, social isolation] remained significantly

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Table 1: Association between socio-demographic factors and nutritional status (MNA categories).

Variables N malnourished % at risk of malnutrition % wellnourished % p

Age mean (SD) 593 78.0 (7.3) 75.4 (6.9) 74.1 (6.7) < 0.001

Age Class 593

65-75 years 341 20 (5.9%) 110 (32.3%) 211(61.8%) < 0.001

76-85years 195 23 (11.8%) 83 (42.6%) 89 (45.6%)

>85years 57 10 (17.5%) 17 (29.8%) 30 (52.7%)

Mohafazat 595

Mountlebanon 144 8 (5.6%) 57 (39.6%) 79 (54.8%) < 0.001

North Lebanon 149 2(1.3%) 52(34.9%) 95 (63.8%)

Bekaa 73 14(19.2%) 23 (31.5%) 36 (49.3%)

South Lebanon 104 26 (25.0%) 40 (38.5%) 38 (36.5%)

Nabatieh 125 4(3.2%) 38 (30.4%) 83 (66.4%)

Living alone 595

Yes 89 5(5.6%) 34(38.2%) 50(56.2%) 0.442

No 506 49 (9.7%) 176 (34.8%) 281(55.5%)

Marital status 596

Married 264 21(8.0%) 74(28.0%) 169(64.0%) < 0.001

Divorced/single 55 1(1.8%) 18(32.7%) 36(65.5%)

Widowed 276 32(11.6%) 118(42.8%) 126 (45.6%)

Education 595

Illiterate 350 45(12.9%) 135(38.6%) 170 (48.5%) < 0.001

Primary school 157 9(5.7%) 54(34.4%) 94(59.9%)

Middle school 65 0(0.0%) 16(24.6%) 49(75.4%)

High school/university 23 0(0.0%) 5(21.7%) 18(78.3%)

Monthly income 559

< 300.000 LL 323 27 (8.4%) 129 (39.9%) 167 (51.7%) 0.027

300.000 – 600.000 LL 133 9 (6.8%) 44 (33.1%) 80 (60.2%)

>600.000LL 103 12 (11.7%) 24 (23.3%) 67 (65.0%)

Social isolation 591

Yes (Lubben score < 12) 291 43 (14.8%) 127 (43.6%) 121(41.6%) < 0.001

No (Lubben score ≥ 12) 300 11 (3.7%) 82 (27.3%) 207 (69.0%)

Abbreviations: N=Number of respondents to this question; SD = Standard Deviation; LL = Lebanese Pound

associated with the outcome variable.

After entering BMI and the four variables related to food intake [model 3], no major changes appeared regarding the previous associations except for widowhood, who lost its relationship with nutritional status [p=0.164]. In addition, preparing meals was associated with an important decrease in nutritional risk [ORa: 032; 95% CI: 0.19-0.53] while, “eating rarely or never with others” was related to a nearly 1.5 fold increased risk of poor nutritional status.

The final model [model 4] displays the adjusted association between poor nutritional status and the previously defined

significantly associated variables. In this model the Nagelkerke R2 was 0.45 meaning that 45% of the variability of the outcome variable [poor nutritional status] was explained by the combination of the introduced independent variables. Among sociodemographic factors, both categories of educational status, primary school and middle school or above, remained strongly related to a gradual decrease of nutritional risk [ORa: 0.58; 95% CI: 0.35-0.96 and ORa: 0.42; 95% CI: 0.21-0.83 respectively] when compared to the category of illiterate women. Furthermore, social isolation was associated with a nearly 2 fold increased risk of malnutrition [p =0.007]. Among health status, all introduced variables showed an independent association with nutritional

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Table 2: Association between healths related characteristics and nutritional status (MNA categories).

Variables N malnourished % at risk of malnutrition % Wellnourished % p

Number of chronic diseases 594≤ 3 diseases 229 11 (4.8%) 58 (25.3%) 160 (69.9%) <0.001>3 diseases 365 43 (11.8%) 152 (41.6%) 170 (46.6%) <0.001

SRH 593Good 190 8 (4.2%) 44 (23.2%) 138 (72.6%) <0.001

Average 249 18 (7.2%) 87 (34.9%) 144 (57.8%)Bad 154 28 (18.2%) 78 (50.6%) 48 (31.2%)

ADL scale 595Dependent for at

l least 1 ADL 157 40(25.5%) 81(51.6%) 36(22.9%) <0.001

Independent 438 14(3.2%) 129(29.5%) 295 (67.3%)Chronic pain 590

Yes 318 42 (13.2%) 128 (40.3%) 148 (46.5%) <0.001No 272 10 (3.7%) 81 (29.8%) 181 (66.5%)

Chewing problems 595Yes 169 21 (12.4%) 66 (39.1%) 82 (48.5%) 0.050No 426 33 (7.7%) 144 (33.8%) 249 (58.5%)

5 item GDS score 590≥ 2 290 44 (15.2%) 142 (49.0%) 104 (35.8%) <0.001< 2 300 6 (2.0%) 67 (22.3%) 227 (75.7%)

MMS 1 mean(SD) 226 15.6 (6.1) 23.3 (4.8) 25.2 (4.2) <0.001MMS 2 mean(SD) 326 11.0 (5.6) 17.6 (5.6) 20.7 (4.9) <0.001

Abbreviations: N=Number of respondents to this question; SRH = Self Related Health; ADL = Activities of Daily Living; GDS score >=2= depression; MMS 1= original version; MMS 2 = adapted for illiteracy

status, while odds ratio of depressive disorders decreased from 3.5 to 1.4 throughout the models. Moreover, close to the results of the previous model, women participating in meal preparation were at lower risk of poor nutritional status [ORa: 0.30; 95% CI: 0.18-0.50], whereas women eating mostly alone had a nearly 50% increased nutritional risk [ORa: 1.50; 95% CI: 1.11-2.02].

Finally, in all the four models age was not associated with nutritional status.

DISCUSSIONThe present study aimed to assess the effect of various

covariates on nutritional status [based on MNA] among a representative sample of rural elderly women in Lebanon. As a main result, we found that education was an important determinant of nutritional status in this female population independently of all other correlates. In fact, throughout the four models, a gradual relationship was found between higher levels of education [versus being illiterate] and reduced risk of poor nutritional status. Similar results were reported in other studies, highlighting the role of education in nutritional health and food choice. For example, in a study including 700 communities dwelling elderly in Northern Italy, lower level of education was associated with a 2.5 fold increased risk of malnutrition among both genders [20]. Furthermore, results from a French cohort study, showed that a “healthy dietary pattern” [fruit, vegetables, grains, nuts, fish] was positively associated with education [21]. Similar results were also reported among Japanese pregnant women, where education, but not household income, was associated with dietary intake [22]. An interesting explanation is given by Galobardes et al. [23], who considered that education

is linked to diet through knowledge and attitudes while income reflects financial means and occupation. As mentioned by this author “the knowledge and skills attained through education may affect a person’s cognitive functioning, make them more receptive to health education messages”. This is of particular importance among women of developing countries. In fact, higher education may increase the autonomy of women and participation in decision making process [24]. In this matter, national representative data regarding women in Ghana showed that participation in decisions regarding household purchases was significantly related to higher dietary diversity, after adjusting for individual and household level covariates [25].

In our study income was not associated with nutritional status in multivariate analysis, unlike the results found among the the basic sample of the AMEL study, where both gender were included [11]. However the present results have to be interpreted with caution because due to their traditional roles, women may not be informed accurately regarding the financial conditions of the household [2].

Among the other sociodemographic factors, being widowed [vs being married] was associated with malnutrition in the 1st and 2nd model. However, when entering the variable “sharing meals” the relationship between widowhood and nutritional status lost its significance. This means that the initial relationship was mainly explained by the fact that married people are eating together, which in turn has a positive impact on food consumption. Accordingly, in a study including homebound elderly people, food intake was increased by 114 kcal per meal in presence of others [26]. This may be partially explained by the duration of the meal,

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Table 3: BMI and food related characteristics distributed by nutritional status (MNA categories).

Variables N malnourished % at risk of malnutrition % wellnourished % p

BMI 586

< 21 40 12 (30.0%) 21 (52.5%) 7 (17.5%) <0.001

21-24.9 120 12 (10.0%) 39 (32.5%) 69 (57.5%)

25-29.9 230 15 (6.5%) 73 (31.7%) 142 (61.8%)

≥30 196 10 (5.1%) 75 (38.3%) 111 (56.6%)

Restrictive diet 592

Yes 310 39 (12.6%) 121(39.0%) 150 (48.4%) <0.001

No 282 14 (5.0%) 88 (31.2%) 180 (63.8%)

Low salt diet 594

Yes 220 32 (14.5%) 94 (42.7%) 94 (42.8%) <0.001

No 374 22 (5.9%) 116 (31.0%) 236 (63.1%)

Sharing meal with others 593

Most of the time 417 28 (6.7%) 138 (33.1%) 251(60.2%) 0.003

Half the time 91 13 (14.3%) 34 (37.4%) 44 (48.3%)

Infrequently/never 85 13(15.3%) 36 (42.4%) 36 (42.3%)

Preparing meals 595

Yes 407 12 (2.9%) 115 (28.3%) 280 (68.8%) <0.001

No/ rarely 188 42 (22.3%) 95 (50.5%) 51 (27.2%)

Abbreviations: N=Number of respondents to this question; BMI = Body Mass Index

Table 4: Binary logistic regression models for malnourished/at risk of malnutrition versus well-nourished.Measures Model 1¹ Model 2² Model 3³ Model 4⁴

ORa (95% IC) p ORa (95% IC) p ORa (95% IC) p ORa (95% IC) pSocio demographic indicators

Age 1.017(0.988-1.046) 0.255 0.998

(0.964-1.032) 0.892 0.979 (0.944-1.016) 0.265 0.983

(0.949-1.018) 0.337

Mohafaza 0.0111.003

(0.586-1.716) 0.992

1.326(0.714-2.466) 0.372

1.717(0.938-3.141) 0.079

0.587(0.338-1.020) 0.059

Marital Status 0.001 0.011 0.031

Divorced/single vs married 0.847(0.431-1.661) 0.628 0.553

(0.245-1.252) 0.156 0.482 (0.208-1.118) 0.089

Widowed vs married 1.981(1.341-2.925) 0.001 1.616

(1.042-2.506) 0.032 1.403 (0.871-2.262) 0.164

Education <0.001 0.010 0.030 0.034

Primary school vs illiterate 0.622(0.408-0.948) 0.027 0.556

(0.342-0.903) 0.018 0.536 (0.321-0.893) 0.017 0.580

(0.351-0.958) 0.033

Middle school or above vs illiterate

0.329(0.184-0.588) <0.001 0.439

(0.222-0.868) 0.018 0.437 (0.203-0.939) 0.034 0.417

(0.194-0.896) 0.025

Lubben scale 2.828(1.931-4.141) <0.001 2.297

(1.488-3.545) <0.001 1.971 (1.247-3.115) 0.004 1.861

(1.185-2.923) 0.007

Physical and mental health statusComorbidities (˃ 3 versus ≤ 3)

2.226(1.390-3.566) 0.001 2.257

(1.371-3.716) 0.001 2.348 (1.435-3.843) 0.001

Chronic pain (yes vs no) 1.854 (1.190-2.888) 0.006 1.713

(1.076-2.726) 0.023 1.766 (1.116-2.795) 0.015

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ADL-Disability (yes vs no) 1.496 (1.246-1.796) <0.001 1.448

(1.202-1.743) <0.001 2.348 (1.435-3.843) <0.001

MMS (quartiles)⁵ 0.001 0.005 0.003

2nd quartile vs 1st quartile 0.619 (0.357-1.075) 0.088 0.705

(0.394-1.262) 0.240 0.773 (0.436-1.370) 0.379

3rd quartile vs 1st quartile 0.316 (0.170-0.588) <0.001 0.350

(0.181-0.674) 0.002 0.349 (0.183-0.669) 0.001

4th quartile vs 1st quartile 0.371 (0.197-0.696) 0.002 0.414

(0.213-0.802) 0.009 0.422 (0.220-0.812) 0.010

5 item GDS score ⁶ 3.532 (2.303-5.416) <0.001 3.458

(2.220-5.387) <0.001 1.433 (1.192-1.724) <0.001

Nutrition and Food related variables

BMI (continuos var) 0.953 (0.919-0.988) 0.009 0.954

(0.921-0.989) 0.011

Restrictive diet (yes vs no) 0.865 (0.454-1.649) 0.659

Salt restricted diet (yes vs no)

1.481 (0.916-2.396) 0.109

Cooking and preparing food (mostly vs rarely)

0.319 (0.192-0.531) <0.001 0.304

(0.184-0.501) <0.001

Sharing of meals (less often vs more often)

1.471 (1.074-2.016) 0.016 1.498

(1.113-2.017) 0.008

Abbreviations: ADL = Activities of Daily Living; MMS = Mini Mental Status; GDS = Geriatric Depression Scale; BMI = Body Mass Index¹Model 1 = Logistic regression analysis between poor nutritional status (malnutrition/at risk of malnutrition) and socio-demographic indicators; ²Model 2 = association between poor nutritional status and health related factors adjusted for socio-demographic indicators; ³Model 3= association between poor nutritional status and food related variables adjusted for significant variables of Model 2; ; ⁴Model 4: adjusted for all significantly associated variables ; ⁵ each quartile includes individuals from respective quartiles of MMS 1 ( original form) and MMS 2 (adapted for illiterate): 1st quartile is corresponding to the lowest category of cognitive status ⁶continuous variable: higher score = more depressive disorders

but also by the fact that the person may receive encouragement to eat more [26].

Another frequent condition among elderly people is social isolation, that was associated with a nearly 2 fold increased risk of poor nutritional status. The relationship between social isolation [based on the LSNS 6 scale] and malnutrition has been the subject of an article previously published by the authors including both genders. The results of this study showed that socially isolated elderly had a 1.6 fold increased risk of impaired nutritional status [27]. In addition, in a recent study conducted among Japanese elderly using the same instruments, the authors found a 2.5 fold increased risk of poor nutritional status [malnutrition and risk of malnutrition] in presence of social isolation [28].

Finally, age was associated with nutritional status only in bivariate analysis but in none of the four models of multivariate analysis. This means that in our study sample, age cannot be considered as a causal factor of poor nutritional status, the latter being mainly due to the morbid conditions associated with the aging process. This is in accordance with many previous studies that either failed to show an independent association between age and nutritional status [29] or reported only a weak association between both variables [30].

The relationship between nutritional status and several health indicators such as level of comorbidities and disability is well documented in the literature [31]. In the urban study conducted by Mitri et al. [2016], among Lebanese elderly living in Greater Beirut, low subjective health and ADL disability were both among the main determinant of malnutrition [12]. However, both are linked by a complex interaction and are frequently part

of a vicious circle [32,33]. For example, malnutrition contributes to disability through muscle loss and weakness; in turn, disability increases the risk for malnutrition and reduced food intake, because subjects may be unable to rely on their own for buying and preparing food, as well as eating by their own [34]. Within our population and because of the cross-sectional design, causal relationship cannot be established among these concepts that are linked through complex interactions. This is also the case for depressive and cognitive disorders in relation with poor nutritional status. Depression is often accompanied by anorexia and loss of interest toward food and therefore frequently accompanied by weight loss. On the other hand, lack of intake in nutrients related to brain metabolism, such as Vitamins B 6, B 9 and B 12 may influence neuronal function and secretion of neurotransmitters and thus increase the risk of mood disorders [35,36]. Depression is a frequent condition among our female population and was present in nearly 50 % of the study participants [6]. It was associated with a 1.4 fold increased risk of poor nutritional status. Results are in accordance with those previously reported among Lebanese elderly [11,12] as well as among other elderly populations [37,38]. Reduced appetite, loss of interest in preparing food and decreased pleasure are common symptoms of depression in geriatric patients [39].

Furthermore higher cognitive status was associated with a significant lower risk of malnutrition; in fact, among women who had a MMS score within the highest quartile, the risk of poor nutritional status was lowered by nearly 60%. Accordingly, in a recently published review including longitudinal studies, cognitive decline was an important predictor of malnutrition and was associated with a 1.8 fold increased risk of malnutrition

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[30]. Inversely, decreased intake of essential nutrients for the brain, especially from fruits, vegetables and fish may also affect cognitive functioning as shown in the Three City Study conducted in France [40].

Furthermore participating in preparing food and cooking was related to an important reduction in risk of malnutrition [ORa: 0.30; p <0.001]. Preparing food involves meal planning and choosing food based on the elderly`s food preferences which in turn would increase food intake. Beside this, sharing meals with others is an important point to highlight in our study: in fact, eating mostly alone was related to a 1.5 fold increased risk of impaired nutritional status. Eating can be considered as a social activity and sharing meals gives the possibility of interaction with others [41]. Also, spending more time during meal may increase food consumption [26].

Major strengths of this study include the evaluation of a large representative rural sample with a comprehensive assessment of numerous covariates through specific geriatric scales. Furthermore, participation rate was particularly high [around 95%]. However; several limitations have to be considered. First, the cross-sectional design does not allow drawing any causal relationship. Second, differences in cognitive function may affect accuracy of responses especially due to memory loss. Third, self-reported information and those related to private sphere may suffer from less reliability. Finally, there may be other unrecognized factors and remaining residual bias.

CONCLUSIONTo the best of our knowledge this is the first study reporting

gender-specific data on the relationship between various correlates and risk of malnutrition among a representative sample of elderly women living in rural settings. Education has to be highlighted as one of the most important contributors to malnutrition; higher education [at least middle school] was associated with a nearly 60% reduced risk of poor nutritional status when compared to illiteracy. Other independently associated correlates of poor nutritional status were: social isolation, reporting more than three comorbidities, chronic pain, suffering from disability, depression and cognitive dysfunction. Furthermore, both eating with others and preparing meals were significantly related to a reduced risk of poor nutritional status.

Our findings demonstrate that giving access to education to all girls and women in the Arabic countries have to be a major concern for policymakers in these regions. In fact women’s education is an important key to allow women to participate in decision-making process within the society and improve families’ well-being [42].

Further studies investigating the impact of women’s education on the nutritional and overall health status of other family members would be of interest.

ACKNOWLEDGEMENTSThe authors thank the council of research who partially

funding the study. Furthermore we are thankful for all the fieldworkers participating in the data collection and data entry.

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