Please use this identifier to cite or link to this item: http://dspace.sctimst.ac.in/jspui/handle/123456789/10920
Title: Intracluster correlation estimates from a World Health Organisation STEPwise approach to surveillance (STEPS) survey for cardiovascular risk factors in Vellore, Tamil Nadu, India. Oommen AM1, Mini GK2, George K3.
Authors: Oommen, AM
Mini, GK
George, K
Keywords: Intracluster correlation; Sampling; Surveys
Issue Date: Mar-2019
Publisher: Public Health
Citation: Oommen AM, Mini GK, George K. Intracluster correlation estimates from a World Health Organisation STEPwise approach to surveillance (STEPS) survey for cardiovascular risk factors in Vellore, Tamil Nadu, India. Public Health. 2019 Mar; 168:102-106
Abstract: OBJECTIVES: Most World Health Organisation (WHO) STEPS surveys use cluster sampling to assess the prevalence of risk factors for non-communicable diseases (NCDs) for which design effects need to be estimated using intracluster correlation (ICCs) coefficients, for sample size calculation. Although there are many reports of risk factor surveys reported from developing countries, there are very few reports of ICCs for risk factors for NCDs, which can inform planning the appropriate sample size needed for such surveys. This study reports the ICCs for NCD risk factors, obtained from a WHO STEPS survey conducted in Vellore district, in the state of Tamil Nadu, South India. STUDY DESIGN: Cross-sectional study. METHODS: A cross-sectional study was carried out in 48 urban clusters (wards) and nine rural clusters (villages) between 2011 and 2012, using the WHO STEPS methodology for assessing behavioural, anthropometric, physical and biochemical risk factors. The ICC estimates for various risk factors were obtained using loneway and xtmelogit commands using STATA to study clustering of risk factors. RESULTS: The number of respondents was 6196 adults aged 30-64 years. The median ICC of cardiovascular risk factors in the urban area was 0.046, while it was 0.064 in the rural area. Clustering was higher for behavioural risk factors such as physical activity (ICC: 0.179 rural, 0.049 urban) and fruit and vegetable intake (ICC: 0.105 rural, 0.091 urban) as compared with physical risk factors (ICCs for hypertension: 0.044 rural, 0.006 urban; body mass index: 0.046 rural, 0.041 urban) and biochemical outcomes such as fasting plasma glucose (ICC: 0.017 rural, 0.027 urban). CONCLUSIONS: This study provides estimates of ICCs for cardiovascular risk factors from Vellore, South India, as such data have not been reported from WHO STEPS surveys in India or neighbouring countries. Such estimates of ICCs if reported from various WHO STEPS being carried out across the country can contribute to better planning of epidemiological surveys. Clustering of behavioural risk factors at village/ward level as seen in this study points to the need for community-based interventions for health promotion, as spatial clustering influences behaviour, which in turn affects chronic disease outcomes.
URI: https://doi.org/10.1016/j.puhe.2018.12.019
http://dspace.sctimst.ac.in/jspui/handle/123456789/10920
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