Search

Background/Objectives Recent evidence suggests nonconstant nature of dispersion in adult women's body mass index (BMI) across sociodemographic groups. The overall variances in BMI and height are also shown to have substantially changed over time. We modeled complex variation in adults' anthropometry--BMI and height--by wealth and education, and assessed their differences over time in India. Subjects/Methods Data from a total of 768,130 women and 180,691 men from the Indian National Family Health Survey (NFHS) 2006 and 2016 were used for the analysis. The average association between wealth and education with anthropometry was assessed from linear regression models assuming constant variance. Individual heterogeneity was modeled to obtain separate variances in anthropometry for each wealth quintile and education level. All analyses were stratified by survey year and sex. Results On average, the positive socioeconomic gradient in adult's BMI and height persisted over time with slight attenuation. The residual variance in BMI ranged from 10.1 to 14.9 (7.2-11.3) by education level and 6.1 to 17.4 (5.0-13.0) by household wealth for women (men) in 2006, and they increased over time for the lower socioeconomic groups but remained the same or decreased for the higher socioeconomic groups. No significant pattern was observed for variation in height for both genders. Conclusions We found potential reversal in the socioeconomic patterning in BMI variability in India as suggested by the increasing dispersion among the least educated and poorest populations. For a comprehensive understanding of nutrition transition in developing countries, it is necessary to assess the changes in means and variances of anthropometry in tandem., Author(s): Rockli Kim [sup.1], Praveen Kumar Pathak [sup.2], Niharika Tripathi [sup.3], S. V. Subramanian [sup.1] [sup.4] Author Affiliations: (1) Harvard Center for Population and Development Studies, Cambridge, USA (2) Department [...]

Abstract. We study the impact of electronic markets on small, boutique firms selling presence goods or services--goods or services that must be consumed at the selling firm's location. These firms [...]

We use panel data from multiple wards from two hospitals spanning a three-year period to investigate the impact of automation of the core error prevention functions in hospitals on medical [...]

This paper provides a methodology for detecting management fraud using basic financial data. The methodology is based on support vector machines. An important aspect therein is a kernel that increases [...]

A study uses genetic programming to examine new ways for effective search and retrieval of information on the web.

The Internet and corporate Intranets have brought a lot of information. People usually resort to search engines to find required information. However, these systems tend to use only one fixed ranking strategy regardless of the contexts. This poses serious performance problems when characteristics of different users, queries, and text collections are taken into account. In this paper, we argue that the ranking strategy should be context specific and we propose a new systematic method that can automatically generate ranking strategies for different contexts based on Genetic Programming (GP). The new method was tested on TREC data and the results are very promising. Index Terms--Intelligent information retrieval, personalization, search engine, term weighting, ranking function, text mining, genetic programming, contextual information retrieval, information routing, information retrieval.