Accurate data on dietary intake are important for public health, nutrition and agricultural policy. The National Sample Survey is widely used by policymakers in India to estimate nutritional outcomes in the country, but has not been compared with other dietary data sources. To assess relative differences across available Indian dietary data sources, we compare intake of food groups across six national and sub-national surveys between 2004 and 2012, representing various dietary intake estimation methodologies, including Household Consumption Expenditure Surveys (HCES), FFQ, food balance sheets (FBS), and 24-h recall (24HR) surveys. We matched data for relevant years, regions and economic groups, for ages 16–59. One set of national HCES and the 24HR showed a decline in food intake in India between 2004–2005 and 2011–2012, whereas another HCES and FBS showed an increase. Differences in intake were smallest between the two HCES (1 % relative difference). Relative to these, FFQ and FBS had higher intake (13 and 35 %), and the 24HR lower intake (−9 %). Cereal consumption had high agreement across comparisons (average 5 % difference), whereas fruit and nuts, eggs, meat and fish and sugar had the least (120, 119, 56 and 50 % average differences, respectively). Spearman’s coefficients showed high correlation of ranked food group intake across surveys. The underlying methods of the compared data highlight possible sources of under- or over-estimation, and influence their relevance for addressing various research questions and programmatic needs.

Dietary patterns analysis is an emerging area of research. Identifying distinct patterns within a large dietary survey can give a more accurate representation of what people are eating. Furthermore, it allows researchers to analyse relationships between non-communicable diseases (NCD) and complete diets rather than individual food items or nutrients. However, few such studies have been conducted in developing countries including India, where the population has a high burden of diabetes and CVD. We undertook a systematic review of published and grey literature exploring dietary patterns and relationships with diet-related NCD in India. We identified eight studies, including eleven separate models of dietary patterns. Most dietary patterns were vegetarian with a predominance of fruit, vegetables and pulses, as well as cereals; dietary patterns based on high-fat, high-sugar foods and more meat were also identified. There was large variability between regions in dietary patterns, and there was some evidence of change in diets over time, although no evidence of different diets by sex or age was found. Consumers of high-fat dietary patterns were more likely to have greater BMI, and a dietary pattern high in sweets and snacks was associated with greater risk of diabetes compared with a traditional diet high in rice and pulses, but other relationships with NCD risk factors were less clear. This review shows that dietary pattern analyses can be highly valuable in assessing variability in national diets and diet–disease relationships. However, to date, most studies in India are limited by data and methodological shortcomings.

Recent global fluctuations in food prices and continuing environmental degradation highlight the future challenge of feeding a growing world population. However, current dialogues rarely address the relationship between agricultural changes and health. This relationship is traditionally associated with the role of food in nutrition and with food safety, and while these are key interactions, we show in this paper that the relationship is far more complex and interesting. Besides the direct effects of agriculture on population nutrition, agriculture also influences health through its impact on household incomes, economies and the environment. These effects are felt particularly in low- and middle-income countries, where dramatic changes are affecting the agriculture–health relationship, in particular the growth of nutrition-related chronic disease and the associated double burden of under- and over-nutrition. Greater understanding of the negative effects of agriculture on health is also needed. While lengthening food value chains make the chain of influence between agricultural policy, food consumption, nutrition and health more complex, there remain opportunities to improve health by changing agricultural systems. The first challenge in doing this, we suggest, is to improve our capacity to measure the impact of agricultural interventions on health outcomes, and vice versa.