Nutrition in India: Facts and Interpretations

Deaton, Angus; Drèze, Jean

doi:10.2139/ssrn.1135253

Cited by 339 publications

(420 citation statements)

References 33 publications

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“…Smoking is a major cause of atherosclerosis, and doubles the chances of mortality from heart diseases. 56 India is not just the diabetes capital of the world with more than 50 million patients, it also has the highest prevalence of metabolic syndrome and obesity e 20 million Indians are obese today with 70 million projected by 2025; 20% of Indians suffer from hypertension. 57 These factors together have paced the growth of cardiovascular diseases in India as well as the mortality levels from these diseases.…”

Section: Indian Scenario Of Cardiovascular Diseasesmentioning

confidence: 99%

Cardiovascular disease risk factors in Asian Indian population: A systematic review

Nag

Ghosh

2014

Journal of Cardiovascular Disease Research

118

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a b s t r a c tBackground: Cardiovascular diseases (CVDs) are the number one cause of death globally and are the leading cause of death in India also. Several surveys conducted across the country over the past few decades have shown a rising prevalence of major risk factors for CVD in Asian Indian population. The problem of increasing risk factors for CVD in India is because of lack of surveillance system and lack of proper diagnosis. This study will help to point out the need of research so that some advanced diagnosis system may be developed for proper diagnosis of CVDs and to reduce the growing burden of CVDs in the country. Methods: We did a literature search for the period from 1968 to 2012 using PUBMED search to identify all relevant studies of cardiovascular diseases. Besides PUBMED searching, manual searching has also been done. This article provides a review of current understanding of the epidemiology of cardiovascular disease, particularly, coronary heart disease (CHD), stroke and related risk factors in Asian Indian population. Results: Hypertension and diabetes are highly prevalent among Asian Indian population, which may explain their high rate of stroke and heart attack in India. The increasing rate of CVD may be explained by the high rates of other risk factors including adverse lipid profile. The etiology of cardiovascular diseases (CVD) is multifactorial and no single factor is an absolute cause. Conclusion: The cardiovascular diseases and its risk factors are increasing with a rapid pace in Asian Indian population. Though the prevalence of CVD risk factors is found higher in urban population, yet it is increasing at an alarming rate in rural population also, which is a serious threatening to the nation. Since majority of the Indians live in rural area, CVD may lead to epidemic proportions. We need health promotion programs and reorientation of primary health care to improve CVD detection in earlier stage and its management.

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Section: Indian Scenario Of Cardiovascular Diseasesmentioning

confidence: 99%

Cardiovascular disease risk factors in Asian Indian population: A systematic review

Nag

Ghosh

2014

Journal of Cardiovascular Disease Research

118

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“…National surveys even show a steady decline in per capita consumption during this period (Chattapadhyay and Sibrian, 2010). A thorough analysis to explain this paradox is urgent, also because the development in India is of great significance for global trends (Deaton and Drèze, 2009; also see further discussion below).…”

Section: Mean Dietary Energy Supply (Des)mentioning

confidence: 99%

“…First, it is measuring directly what we are essentially interested in, namely how undernutrition affects the health and well-being of individuals. There is overwhelming evidence that undernutrition that generates poor anthropometric outcomes contributes to higher morbidity and mortality (Pelletier, 1994;Klasen, 2008;Deaton and Drèze, 2009). Second, the availability of three different child anthropometric indicators gives a good sense of both chronic and acute undernutrition.…”

Section: Strengths and Weaknesses Of Anthropometric Approachesmentioning

confidence: 99%

“…Fourth, small sample sizes often preclude careful disaggregation of anthropometrics by sub-groups or regions or make them statistically unreliable. Related to this are the usual problems of household surveys including issues of drawing adequate samples in countries where census counts are often not reliable (Deaton and Drèze, 2009). Fifth, in spite of their comprehensiveness, DHS and MICS lack some critical variables to better analyze determinants of undernutrition; most seriously, data on household income or expenditures are not included.…”

Section: Strengths and Weaknesses Of Anthropometric Approachesmentioning

confidence: 99%

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What do we really know? Metrics for food insecurity and undernutrition

2011

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in What do we really know? Metrics for food insecurity and undernutritionAbstract. In this article, we critically review the three most common approaches of assessing chronic food insecurity and undernutrition: (i) the FAO indicator of undernourishment, (ii) household food consumption surveys, and (iii) childhood anthropometrics. There is a striking and worrying degree of inconsistency when one compares available estimates, which is due to methodological and empirical problems associated with all three approaches. Hence, the true extent of food insecurity and undernutrition is unknown. We discuss strengths and weaknesses of each approach and make concrete suggestions for improvement, which also requires additional research. A key component will be the planning and implementation of more comprehensive, standardized, and timely household surveys that cover food consumption and anthropometry, in addition to other socioeconomic and health variables.Such combined survey data will allow much better assessment of the problems' magnitude, as well as of trends, driving forces, and appropriate policy responses.

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“…However, this explanation does not solve the contradiction in trends between the household survey and the FBS figures, since both should presumably reflect changes in age structure. Deaton and Drèze (2009) examine recent trends in per capita food consumption in India based on household survey data and find a pattern similar to China. They suggest that one plausible hypothesis for India is that per capita caloric requirements have fallen due to declining levels of physical activity and possibly also improvements in health.…”

Section: Introductionmentioning

confidence: 99%

Matching food with mouths: A statistical explanation to the abnormal decline of per capita food consumption in rural China

Abler

2016

Food Policy

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. This study provides an alternative explanation for the unusual apparent decline in food consumption in rural China after 2000. We find that it is mainly attributable to significant measurement errors in the Chinese Rural Household Survey and the calculation of per capita food consumption. In a household survey, total consumption for a household in a certain period is often well recorded, and per capita consumption is obtained by dividing total consumption by household size. Such a calculation of per capita food consumption is vulnerable to a mismatch between food and mouths. Total consumption may be subject to measurement errors caused primarily by food away from home (FAFH). Also, the household size recorded in the survey is not necessarily the same as the number of mouths (consumption household size), who consume the food recorded in the survey. Our results indicate that food consumption in rural China is currently being underestimated by about 30%. Our results also indicate that income elasticities of food consumption are greater than measured elasticities based on the Rural Household Survey data. Terms of use: Documents in

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Nutrition in India: Facts and Interpretations

Abstract: Calories (kc) Protein (gms) Fats (gms) Year Round Rural Urban Rural Urban Rural Urban

Cited by 339 publications

References 33 publications

Cardiovascular disease risk factors in Asian Indian population: A systematic review

Cardiovascular disease risk factors in Asian Indian population: A systematic review

What do we really know? Metrics for food insecurity and undernutrition

Matching food with mouths: A statistical explanation to the abnormal decline of per capita food consumption in rural China

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