Literature DB >> 27681551

Influence of socioeconomic aspects on lymphatic filariasis: A case-control study in Andhra Pradesh, India.

Srinivasa Rao Mutheneni1, Suryanaryana Murty Upadhyayula1, Sriram Kumaraswamy1, Madhusudhan Rao Kadiri1, Balakrishna Nagalla2.   

Abstract

BACKGROUND &
OBJECTIVES: Lymphatic filariasis (LF) is a major public health problem in India. The objective of the study was to assess the impact of socioeconomic conditions on LF in Chittoor district of Andhra Pradesh, India.
METHODS: A survey was carried out from 2004 to 2007 during which, an epidemiological and socioeconomic data were collected and analysed. The microfilaria (mf) positive samples were taken as cases and matched with control group by sex and age (1:1) for case-control study. Bivariate and multivariate logistic regression was used to identify the potential risk factors for filariasis. Using principal component analysis (PCA), a socioeconomic index was developed and the data/scores were classified into low, medium and high categories.
RESULTS: In total 5,133 blood smears were collected, of which 77 samples were found positive for microfilaria (1.52%). Multivariate analysis showed that the risk of filariasis was higher in groups of people with income < ₹1000 per month [OR = 2.752 (95%CI, 0.435-17.429)]; ₹ 1000-3000 per month [3.079 (0.923-0.275)]; people living in tiled house structure [1.641 (0.534-5.048)], with kutcha (uncemented) drainage system [19.427 (2.985- 126.410)], respondents who did not implemented mosquito avoidance measures [1.737 (0.563-5.358)]; and in people who were not aware about prevention and control of filariasis [1.042 (0.368-2.956)]. PCA showed that respondents with low (41.6%) and medium (33.8%) socioeconomic status are more prone to filariasis (p=0.036). INTERPRETATION &amp;
CONCLUSION: The cross sectional study showed that the population with low and medium socioeconomic status are at higher risk of filariasis. The identified socioeconomic risk factors can be used as a guideline for improving the conditions for effective management of filariasis.

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Mesh:

Year:  2016        PMID: 27681551

Source DB:  PubMed          Journal:  J Vector Borne Dis        ISSN: 0972-9062            Impact factor:   1.688


  5 in total

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2.  Applications of machine learning techniques to predict filariasis using socio-economic factors.

Authors:  Phani Krishna Kondeti; Kumar Ravi; Srinivasa Rao Mutheneni; Madhusudhan Rao Kadiri; Sriram Kumaraswamy; Ravi Vadlamani; Suryanaryana Murty Upadhyayula
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  5 in total

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