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Disentangling women's responses on complex dietary intake patterns from an Indian cross-sectional survey: a latent class analysis

Published online by Cambridge University Press:  02 January 2007

Sabu S Padmadas ,
José G Dias  and
Frans J Willekens
Sabu S Padmadas*
Affiliation:
Division of Social Statistics & Southampton Statistical Sciences Research Institute, University of Southampton, Highfield, Southampton, SO17 1BJ, UK
José G Dias
Affiliation:
Department of Quantitative Methods, Instituto Superior de Ciencias do Trabalho e da Empresa (ISCTE), Lisbon, Portugal
Frans J Willekens
Affiliation:
Netherlands Interdisciplinary Demographic Institute, The Hague, The Netherlands and Population Research Centre, University of Groningen, The Netherlands
*
*Corresponding author: Email [email protected]
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Abstract

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Objective

To investigate the degree of individual heterogeneity related to complex dietary behaviour and to further examine the associations of different dietary compositions with selected characteristics.

Design

Latent class analysis was applied to data from the recent cross-sectional National Family Health Survey that collected information on the intake frequency of selected foods. Different responses regarding intake frequency were condensed into a set of five meaningful latent clusters representing different dietary patterns and these clusters were then labelled based on the reported degree of diet mixing.

Setting

Indian states.

Subjects

In total, 90 180 women aged 15–49 years.

Results

Three clusters were predominantly non-vegetarian and two were vegetarian. A very high or high mixed-diet pattern was observed particularly in the southern and a few north-eastern states. Many women in the very high mixed-diet cluster consumed mostly non-green/leafy vegetables on a daily basis, and fruits and other non-vegetarian diet on a weekly basis. In contrast, those in the low mixed-diet cluster consumed more than three-fifths of the major vegetarian diet ingredients alone on a daily basis. The affluent group that represented the low mixed-diet cluster were primarily vegetarians and those who represented the very high mixed-diet cluster were mostly non-vegetarians. The significant interrelationships of different characteristics highlight not only socio-economic, spatial and cultural disparities related to dietary practices, but also the substantial heterogeneity in diet mixing behaviour.

Conclusions

The results of this study confirmed our hypothesis of heterogeneous dietary behaviour of Indian women and yielded useful policy-oriented results which might be difficult to establish otherwise.

Keywords

Dietary patternsWomenIndiaLatent class modelUnobserved heterogeneityNational Family Health Survey
Type
Research Article
Information
Public Health Nutrition , Volume 9 , Issue 2 , April 2006 , pp. 204 - 211
Copyright
Copyright © The Authors 2006

References

1Woods, SC, Schwartz, MW, Baskin, DG, Seeley, RJ. Food intake and the regulation of body weight. Annual Review of Psychology! 2000; 51: 255–77.CrossRefGoogle ScholarPubMed
2Kakwani, N. On Specifying Poverty Lines. Asia and Pacific Forum on Poverty. Manila: Asian Development Bank, 2001.Google Scholar
3Popkin, BM. The shift in stages of the nutrition transition in the developing world differs from past experiences. Public Health Nutrition 2002; 5: 205–14.CrossRefGoogle ScholarPubMed
4Shetty, PS. Nutrition transition in India. Public Health Nutrition 2002; 5: 175–82.CrossRefGoogle ScholarPubMed
5Srinkantia, SG. The National Nutrition Monitoring Bureau. Hyderabad: Nutrition Foundation of India, 1998.Google Scholar
6National Institute of Nutrition. Nutrition in India: UN/ACC/SCN Country Case Study Supported by UNICEF. Hyderabad: National Institute of Nutrition, 1992.Google Scholar
7Shariff, A, Mallick, AC. Dynamics of food intake and nutrition by expenditure class in India. Economic and Political Weekly 1999; 34: 1790–800.Google Scholar
8International Institute for Population Sciences (IIPS). National Family Health Survey (NFHS-2), 1998–99. Mumbai/Washington, DC: IIPS/ORC Macro, 2000.Google Scholar
9Reddy, KS. Cardiovascular diseases in the developing countries: dimensions, determinants, dynamics and directions for public health action. Public Health Nutrition 2002; 5: 231–7.CrossRefGoogle ScholarPubMed
10Hu, FB. Diet, lifestyle, and risk of type 2 diabetes mellitus in women. New England Journal of Medicine 2001; 345: 790–7.CrossRefGoogle ScholarPubMed
11Gopalan, C. Diet related non-communicable diseases in South and South East Asia. In: Shetty, PS, McPherson, K, eds. Diet, Nutrition and Chronic Disease: Lessons from Contrasting Worlds. London: John Wiley & Sons, 1997; 10–23.Google Scholar
12Chadha, SL, Gopinath, N, Shekawat, S. Urban–rural differences in the prevalence of coronary heart disease and its risk factor in Delhi. Bulletin of the World Health Organization 1997; 75: 31–8.Google ScholarPubMed
13Kant, AK, Schatzkin, A, Ziegler, RG. Dietary diversity and subsequent cause-specific mortality in the NHANES I epidemiological follow-up study. Journal of the American College of Nutrition 1995; 14: 233–8.CrossRefGoogle Scholar
14Clogg, CC. Latent class models. In: Arminger, G, Clogg, CC, Sobel, ME, eds. Handbook of Statistical Modelling for the Social and Behavioral Sciences, Volume 14. New York: Plenum, 1995; 233–8.Google Scholar
15Goodman, LA. Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika 1974; 61: 215–31.CrossRefGoogle Scholar
16Patterson, BH, Dayton, CM, Graubard, BI. Latent class analysis of complex survey data: application to dietary data. Journal of the American Statistical Association 2002; 97: 721–9.CrossRefGoogle Scholar
17Lazarsfeld, PF, Henry, NW. Latent Structure Analysis. New York: Houghton Mifflin, 1968.Google Scholar
18Dempster, AP, Laird, NM, Rubin, DB. Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society 1977; B39: 1–38.Google Scholar
19McLachlan, GJ, Peel, D. Finite Mixture Models. New York: John Wiley & Sons, 2000.CrossRefGoogle Scholar
20Akaike, H. A new look at statistical model identification. IEEE Transactions on Automation and Control 1974; 19: 716–23.CrossRefGoogle Scholar
21Schwarz, G. Estimating the dimension of a model. Annals of Statistics 1978; 6: 461–4.CrossRefGoogle Scholar
22Bozdogan, H. Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika 1987; 52: 345–70.CrossRefGoogle Scholar
23Wedel, M, Kamkura, W. Market Segmentation: Conceptual and Methodological Foundations. Boston, MA: Kluwer Academic Publishers, 1998.Google Scholar
24Vermunt, JK, Magidson, J. Latent class models for classification. Computational Statistics and Data Analysis 2003; 41: 531–7.CrossRefGoogle Scholar
25Dréze, J, Sen, A. The Political Economy of Hunger.Vol.III. Endemic Hunger. Oxford: Oxford University Press. 1991.CrossRefGoogle Scholar
26Achaya, KT. Fat Intakes in India. Hyderabad: Nutrition Foundation of India, 1986.Google Scholar
27Food and Agricultural Organization of the United Nations (FAO). FAO Nutrition Country Profile of India. Rome: FAO, 1998.Google Scholar
28Sinha, R, Anderson, DE, McDonald, SS, Greenwald, P. Cancer risk and diet in India. Journal of Postgraduate Medicine 2003; 49: 222–8.Google ScholarPubMed
29Hatløy, 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. European Journal of Clinical Nutrition 1998; 52: 891–8.CrossRefGoogle Scholar
30Drewnowski, A, Henderson, SA, Shore, AB, Fischler, C, Preziosi, P, Hercberg, S. Diet quality and dietary diversity in France: implications for the French paradox. Journal of the American Dietetic Association 1996; 96: 663–9.CrossRefGoogle ScholarPubMed
31Griffiths, PL, Bentley, ME. The nutrition transition is underway in India. Journal of Nutrition 2001; 131: 2692–700.CrossRefGoogle ScholarPubMed