|Year : 2018 | Volume
| Issue : 4 | Page : 296-301
Dietary diversity and its determinants: A community-based study among adult population of Durgapur, West Bengal
Archan Mukherjee1, Sourabh Paul2, Indranil Saha1, Tapas Kumar Som1, Gautam Ghose1
1 Department of Community Medicine, IQ City Medical College, Durgapur, West Bengal, India
2 Department of Community Medicine, Adesh Institute of Medical Science and Research Barnala Road, Bathinda, Punjab, India
|Date of Web Publication||2-Aug-2018|
P-19, Jadavpur University Employees' Housing Co-operative Society Ltd. P.O. – Panchasayar, Kolkata - 700 094, West Bengal
Source of Support: None, Conflict of Interest: None
Introduction: Proper diet is essential from the very early stages of life for proper growth and development. Increasing the variety of foods and food groups in the diet helps to ensure adequate intake of essential nutrients. The study was conducted with the objective to assess the dietary diversity pattern and to find the association between dietary diversity score and selected sociodemographic variables among adult population, if any. Materials and Methods: A community-based cross-sectional study was conducted between December 2016 and January 2017 among 216 adults with the help of dietary diversity questionnaire from rural and urban field practice area of the Department of Community Medicine, IQ City Medical College, Durgapur, West Bengal, India. Pearson's Chi-square test, Mann–Whitney U-test, and binary multivariable logistic regression analysis were performed using SPSS software. Results: The median dietary diversity score of the participants was 6. Nearly 45.4% of participants had adequate dietary diversity scores. Most common food groups consumed by the participants were starchy staples (100%), followed by oil and oil-based items (99.5%) and milk and milk products (86.1%). Age, residency, type of family, and occupation have a significant association with adequate dietary diversity. Conclusion: Awareness program on dietary diversity should be organized to make people aware about the importance of dietary diversity. Proper diet is essential from the very early stages of life for proper growth and development. Increasing the variety of foods and food groups in the diet helps to ensure adequate intake of essential nutrients.
Keywords: Adult, community, determinants, dietary diversity, dietary diversity score
|How to cite this article:|
Mukherjee A, Paul S, Saha I, Som TK, Ghose G. Dietary diversity and its determinants: A community-based study among adult population of Durgapur, West Bengal. Med J DY Patil Vidyapeeth 2018;11:296-301
|How to cite this URL:|
Mukherjee A, Paul S, Saha I, Som TK, Ghose G. Dietary diversity and its determinants: A community-based study among adult population of Durgapur, West Bengal. Med J DY Patil Vidyapeeth [serial online] 2018 [cited 2020 Oct 25];11:296-301. Available from: https://www.mjdrdypv.org/text.asp?2018/11/4/296/238159
| Introduction|| |
Nutrition is a basic human need and a prerequisite to a healthy life. A proper diet is essential from the very early stages of life for proper growth and development. The World Health Organization has suggested that at least 20, perhaps as many as 30, biologically distinct variants of foods should be consumed each week for a healthy diet. Dietary diversity is defined as a number of food groups consumed over a reference period. It reflects the concept that increasing the variety of foods and food groups in the diet helps to ensure adequate intake of essential nutrients. Food diversity adds several dimensions to human health. It encourages biodiversity and sustainability, allows for nutritional adequacy, minimizes adverse consequences of food on health, provides interest in food, and finally reduces the prevalence of chronic diseases. Several studies have shown that the overall nutritional quality of the diet is improved with diverse diet., Nondiversified diet can have negative consequences on individuals' health, well-being, and development, not only by reducing physical capacities and resistance to infection but also by impairing cognitive development, reproductive, and even social capacities. Dietary problems are measured mainly quantitatively in majority of the underprivileged third world countries; however, the question of dietary diversity or food variety is also equally important. Due to the perceived importance of dietary diversity for health and nutrition, indicators of dietary diversity have become increasingly popular in recent years. The gold standard for measuring dietary diversity is by measuring various food items consumed by the individual or more preferably on terms of kilocalorie intake. Although the Food Consumption Score is a standard method, it has some disadvantages such as it is a more thorough process and time-consuming and requires technical expertise. The Individual Dietary Diversity Score (IDDS) is a proxy measure which aims to reflect nutrient adequacy, nutrient quality, and probability of micronutrient intake and has been validated for several age group and sex in different parts of the world. In India, there are a very limited number of studies focusing on dietary diversity using IDDS. In this background, the present study was conducted with the objective to assess the dietary diversity pattern and to find the association between dietary diversity score and selected sociodemographic variables among adult population, if any.
| Materials and Methods|| |
The present study was a community-based observational epidemiological study with a cross-sectional design conducted between December 2016 and January 2017. The study areas were Damodar Valley Corporation (DVC) junction of Bhavani Pally and Jamua village of Durgapur, district West Burdwan, West Bengal, India. DVC more is urban while Jamua village is rural field practice area of the Department of Community Medicine, IQ City Medical College and Narayana Hrudayalaya Hospital, Durgapur. Individuals aged 18 years and above residing permanently in the study area for the Past 2 years and who gave written informed consent were included in the study. People who had fast or feast in the previous day of data collection was excluded from the study. A study conducted in Bangladesh, 2009, had found that mean DDS was 4.5 ± 1.1. So using the formula N = 4σ2/L2 (N = sample size, σ = standard deviation, and L = allowable error), sample size came out to be 208 (standard deviation = 1.1, allowable error = 0.16, and nonresponse rate = 10%). Altogether, 216 participants were studied. Participants were selected from rural and urban area at 1:1 ratio. All the adult members were considered to form the sampling frame, and among them, eligible adult person was taken as study individuals using convenience sampling technique method from different houses till the desired sample size was reached for urban and rural areas separately.
A predesigned, prestructured standardized pro forma was used for data collection. Pro forma had two parts: first part contained demographic details of the participants and second part contained dietary diversity questions. Dietary diversity questionnaire is a standardized questionnaire developed by the Food and Agriculture Organization of the United Nations. The modified BG Prasad Classification was used for assessing the socioeconomic status of the participants. Participants were interviewed using 24-h recall method. The food items were classified into 16 different food groups, and the respective serial numbers were allotted to them. For example, food items such as corn/maize, rice, wheat, sorghum, and millet fall under food group “cereals” in serial number 1. White potatoes, white yam, and white cassava fall under food group “white roots and tubers” in serial number 2, and in this way, the process goes on. Later on, 16 different food groups were combined to 9 mutually exclusive food groups. Consumption of each food group item was rewarded with 1 point. Hence, maximum possible dietary diversity score was 9. Adequate dietary diversity was defined as IDDS score ≥7, based on data distribution in our findings.
All the data were entered into a Microsoft Excel worksheet (Microsoft, Redwoods, WA, USA) and were analyzed using SPSS software (Statistical Package for the Social Sciences Inc., Chicago, IL, USA), version 21.0. For descriptive statistics, categorical data were expressed in proportions. For continuous data, it was first checked for normality distribution by Kolmogorov–Smirnov test, where significant P value indicated skewed distribution of dataset. Thus, median and interquartile range (IQR) were used for central tendency and dispersion of continuous data. Significance of association between the two attributes was analyzed using Pearson's Chi-square test statistic, and Mann–Whitney U-test was employed to compare the overall distribution of values between two groups. Binary multivariable logistic regression analysis was done to find the sociodemographic predictors of IDDS score. Dependent variable was IDDS and it was categorized as adequate (7–9) and inadequate (1–6). Adequate dietary diversity score was coded as 0 and inadequate dietary diversity score as 1. The entire demographic variables (e.g., age, gender, type of family, number of family members, educational status, occupation, and monthly family income) were considered as independent variable for binary logistic regression. All the P < 0.05 was considered as statistically significant. During the whole period of research, we followed the ethical guidelines mentioned by ICMR. Inform written consent was taken from the participants. Confidentiality and anonymity was maintained during the entire study period.
| Results|| |
A total number of participants were 216. Among them, 107 (49.5%) were from rural area and 109 (50.5%) were from urban area. The mean age of the participants was 39.58 ± 14.1 years and median was 38 years (IQR: 30–48 years). The median age of the rural participants was 40 years (IQR: 32–54 years), whereas in urban, it was 35 years (IQR 26–45 years).
Majority of the participants in rural area were male (71%), whereas in urban area, it was female (60.6%). According to the type of family, there was no much difference between rural and urban participants. In urban area, more than three-fourth of the participants had above primary level education whereas it was just above 60% for the rural. In respect of occupation and socioeconomic status, there was not much difference between rural and urban participants [Table 1].
Urban participants' main food procurement source was purchasing from market (90.8%) followed by own production (5.5%). In case of rural participants, also purchasing (69.2%) was the main source of food procurement followed by own production (22.4%).
Median dietary diversity score of the participants was 6 (IQR: 5–7). The median DDS score of the participants belonging from rural area (median: 7 and IQR: 6–8) was significantly higher compared to urban area (median: 6 and IQR: 5–7), U = 3540 (P = 0.001) (Mann–Whitney U-test was applied because data were not normally distributed). Participants having adequate DDS (DDS ≥7) were 98 (45.4%).
Most common food groups consumed by the participants were starchy staples (100%), followed by oil and oil-based items (99.5%) and milk and milk products (86.1%). A similar trend was followed for both urban and rural participants. However, dark green leafy vegetable (P = 0.001), organ meat and fish (P = 0.001), and egg (P = 0.001) consumption were significantly higher among rural participants compared to urban [Table 2].
|Table 2: Association between consumption of different food groups and area of residency|
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In binary multivariable logistic regression analysis, there was not much multicollinearity among the independent variable because lowest tolerance value is 0.641 and range of variance inflation factor (VIF) is 1.118–1.177. The model was well fitted with a nonsignificant value (P = 0.721) using Hosmer and Lemeshow test. After controlling for the predictors, the model explained between 17.5% (Cox and Snell R2) and 23.4% (Nagelkerke R2) of the variance of DDS in the study participants and correctly classified 69.0% of the cases. Age, residency, type of family, and occupation have a significant association with adequate dietary diversity (DDS ≥7). As the age increases (odds ratio [OR] = 0.97), there is reduction in adequate DDS and the association is significant (P = 0.033). Similarly, participants belonging from rural area and joint family has significantly higher odds (OR = 4.26 and 2.11) of adequate dietary diversity compared to urban and nuclear family (P = 0.001 and 0.048). Participants other than business class has lower odds (OR = 0.39) of adequate dietary diversity compared to business class (P = 0.014). Gender, number of family members, educational status, and socioeconomic status do not hold any significant association with adequate dietary diversity [Table 3].
|Table 3: Binary multivariable logistic regression for association between dietary diversity and sociodemographic factors|
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More than 50% of the participants having adequate dietary diversity were consuming eight different food groups except eggs whereas more than 50% of the participants having inadequate dietary diversity were lacking behind in consuming of dark green leafy vegetables, other vitamin A-rich fruits and vegetables, organ meat and fish, and eggs [Table 4].
|Table 4: Food group consumed by >50% household by dietary diversity classification|
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| Discussion|| |
In resource-poor countries across the globe, low-quality monotonous diets are the norm. Due to the cost and complexity of the quantitative dietary survey, very few developing countries have nationally representative survey to show dietary diversity. The IDDS questionnaire is a tool providing a more rapid, user-friendly, and cost-effective approach to measure changes in dietary quality at individual level. This study had found that mean dietary diversity score of the participants was 6.28 ± 1.3 and it is slightly more compared to a similar study conducted in Bangladesh (mean DDS: 4.5 ± 1.1) which country has similar population characteristics with our study population. Difference might be because of the fact that their study population was only female and setting was only rural. In this study, mean diversity score was more in rural compared to urban area. A similar finding was found in case of a study conducted in rural area of Bangladesh (DDS 4.5 ± 1.1) compared to a periurban area of Philippians (4.2 ± 1.5). Probably, the availability was high and price of the different food items was low in rural area compared to urban which might have reflected as more dietary diversity and high IDDS score compared to urban.,,,, The present study also had highlighted that rural population had consumed significantly more of fish, meat, egg, and green leafy vegetables compared to urban. A report of the Animal Husbandry and Fishery Department 2014 shows that in India, rural area has consumption of 269 g fish/month/person compared to urban 238 g/month/person., A study conducted in Uttar Pradesh also had found that there was a significant difference in consumption of green leafy vegetables, meat, and fish among rural and urban participants. A report of the National Sample Survey Organization (NSSO) also found that in India, rural population has higher proportion of vegetable consumption compared to urban. A report of NSSO shows that egg is consumed more in urban area compared to rural. This is contrast to our study findings. Probably, data collection day in the urban area was a specific day when the participants might not have consumed egg.
Starchy staples were the most common food group in our study population followed by oil-based product and milk. A study conducted in Bangladesh also had similar finding except milk-based product. The difference may be because of the fact the in that study population was mainly females who generally consumes less nutritious diet in our society because of gender bias. Egg, meat, and fish consumption was low in our whole study population which was similar to Bangladesh study findings.
Those participants who had adequate dietary diversity in this study were consuming eight different food groups whereas it was six in Bangladesh study. The difference may be because of different cutoff used for adequacy of dietary diversity.
In this study, age, residency, type of family, and occupation have a significant association with adequate dietary diversity (DDS ≥7). As the age progresses, consumption of variety of food is reduced as a result DDS also reduced. A similar finding was found in two studies conducted in rural area of Mali and Sri Lanka., Studies conducted at Peru  and Ethiopia  had found that dietary diversity is more in urban area compared to rural, which is inverse to our finding. In this study, majority of the urban participants were from lower and lower middle class, so probably, they were consuming less junk foods as a result their dietary diversity might not have increased much. Because studies have shown that the dietary diversity increases with increase junk food consumptions. Studies conducted in Bankura district of West Bengal, an eastern India, had found that dietary diversity increases as the family size increases which is similar to our study finding that joint family has positive association with DDS. In this study, business class has more DDS compared to other occupation which is contrast to Bankura study. A majority of the participants in other occupation of this study were housewives who usually have less variety in diet. This might be the possible reason for contrast finding with other studies. This study also found that gender does have a significant role in dietary diversity which is similar to another studies conducted in Bankura, West Bengal. Probably, in this part of the country, gender bias might be less dominating factor. Education does not play a significant role in this study which is similar to a study conducted among women in Burkina Faso  whereas finding is opposite to many other studies.,, Socioeconomic status is not a significant determinant of DDS in this study which is also paradoxical with many reported studies., In this study, very few participants were belonging from upper and upper middle class according to the BG Prasad Classification, so it might not have a significant impact on DDS.
Due to the short study duration, we were not able to assess the seasonal variation of dietary diversity among the participants. There was no standardized cutoff value for adequate and inadequate dietary diversity which might have influence on the outcome analysis.
| Conclusion|| |
Dietary diversity is one of the key elements of diet quality. The present study has highlighted that half of the population do not reach to their adequate dietary diversity and age, area of belonging, type of family, and occupation are major determinates of dietary diversity. Hence, awareness program on dietary diversity should be organized to make people aware about the importance of dietary diversity.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Arimond M, Wiesmann D, Becquey E, Carriqulry A, Daniels M, Deltchier M, et al
. Dietary Diversity as a Measure of the Micronutrient Adequacy of Women's Diets in Resource-Poor Areas: Summary of Results from Five Sites. Washington, DC: FANTA-2 Bridge, FHI; 2011.
Mirmiran P, Azadbakht L, Esmaillzadeh A, Azizi F. Dietary diversity score in adolescents – A good indicator of the nutritional adequacy of diets: Tehran lipid and glucose study. Asia Pac J Clin Nutr 2004;13:56-60.
Hatløy A, Torheim LE, Oshaug A. Food variety – A good indicator of nutritional adequacy of the diet? A case study from an urban area in Mali, West Africa. Eur J Clin Nutr 1998;52:891-8.
Ogle BM, Hung PH, Tuyet HT. Significance of wild vegetables in micronutrient intakes of women in Vietnam: An analysis of food variety. Asia Pac J Clin Nutr 2001;10:21-30.
Underwood BA. From research to global reality: The micronutrient story. J Nutr 1998;128:145-51.
Allen LH, Black AK, Backstrand JR, Pelto GH, Ely RD, Molina E, et al
. An analytical approach for exploring the importance of dietary quality versus quantity in the growth of Mexican children. Food Nutr Bull 1991;13:95-104.
Martin-Prevel Y, Becquey E, Tapsoba S, Castan F, Coulibaly D, Fortin S, et al.
The 2008 food price crisis negatively affected household food security and dietary diversity in urban Burkina Faso. J Nutr 2012;142:1748-55.
Arimond M, Torheim LE, Wiesmann D, Joseph M, Carriquiry A. Dietary Diversity as a Measure of the Micronutrient Adequacy of Women's Diets: Results from Rural Bangladesh Site. Food and Nutrition Technical Assistance II Project (FANTA-2). Washington DC: FHI; 2009.
Khainar MR, Wadgave U, Shimpi PV. Updated BG Prasad socioeconomic classification for 2016. J Indian Assoc Public Health Dent 2016;14:469-70.
Arimond M, Wiesmann D, Becquey E, Carriquiry A, Daniels MC, Deitchler M, et al.
Simple food group diversity indicators predict micronutrient adequacy of women's diets in 5 diverse, resource-poor settings. J Nutr 2010;140:2059S-69S.
Das DK. Lakshadweep Tops in Fish Consumption. Bengal 4th
. The Times of India. India: The Economic Times; 2014.
Kumar P, Dey MM, Paraguas F. Demand for fish species in India: Three stage budgeting framework. Agric Econ Res Rev 2005;18:167-86.
Majumdar A, Ray R, Sinha K. The calculation of rural urban food price differentials from unit values in household expenditure surveys: A new procedure and comparison with existing methods. Am J Agric Econ 2012;94:1218-35.
Pandey P, Neerubala. Food consumption patterns of adult population in rural and urban areas of faizabad district of Uttarpradesh. Int J Sci Res 2016;5:2080-4.
Khanna R. Food Price Pinch Worst in Kolkata. The Times of India. India: The Economic Times; 2013.
Ministry of Statistics and Programme Implementation: Government of India. Household Consumption of Various Goods and Services in India: 66th
Round (2009-10). New Delhi: National Sample Survey Office (NSSO); 2012.
Torheim LE, Ouattara F, Diarra MM, Thiam FD, Barikmo I, Hatløy A, et al.
Nutrient adequacy and dietary diversity in rural Mali: Association and determinants. Eur J Clin Nutr 2004;58:594-604.
Rathnayake KM, Madushani P, Silva K. Use of dietary diversity score as a proxy indicator of nutrient adequacy of rural elderly people in Sri Lanka. BMC Res Notes 2012;5:469.
Andrissi L, Mottini G, Sebastiani V, Boldrini L, Giuliani A. Dietary habits and growth: An urban/rural comparison in the Andean region of Apurimac, Peru. Ann I st
Super Sanita 2013;49:340-6.
Herrador Z, Perez-Formigo J, Sordo L, Gadisa E, Moreno J, Benito A, et al.
Low dietary diversity and intake of animal source foods among school aged children in Libo Kemkem and Fogera districts, Ethiopia. PLoS One 2015;10:e0133435.
Bezerra IN, Sichieri R. Household food diversity and nutritional status among adults in Brazil. Int J Behav Nutr Phys Act 2011;8:22.
Sarkar S. Household dietary diversity: A study of rural households in West Bengal. Eur Acad Res 2014;2:8307-25.
Parappurathu S, Kumar A, Bantilan MC, Joshi PK. Food consumption patterns and dietary diversity in Eastern India: Evidence from village level studies (VLS). Food Secur 2015;7:1031-42.
Savy M, Martin-Prével Y, Sawadogo P, Kameli Y, Delpeuch F. Use of variety/diversity scores for diet quality measurement: Relation with nutritional status of women in a rural area in Burkina Faso. Eur J Clin Nutr 2005;59:703-16.
[Table 1], [Table 2], [Table 3], [Table 4]