Bull World Health Organ 2010 88:297���304 | doi:10.2471/BLT.09.065847 297 Introduction Over the last few decades, traditional societies in many develop- ing countries have experienced rapid and unplanned urbaniza- tion, which has led to lifestyles characterized by unhealthy nutri- tion, reduced physical activity and tobacco consumption.1 These unhealthy lifestyles are associated with common modifiable risk factors for chronic diseases such as hypertension, diabetes mel- litus, dyslipidaemia and obesity.2 It is expected that by 2020 in developing countries, non- communicable diseases (NCDs) will account for 69% of all deaths, with cardiovascular diseases in the lead.3 The prevalence of diabetes mellitus will almost double in the next 25 years and at least 75% of those affected will be in developing countries. The burden of disease will be worse in these countries, as the majority of sufferers are expected to be relatively young, of lower socioeconomic status and to suffer from severe disease of premature onset.4 Using the dichotomous United Nations definition of ur- banization (based on country specific definitions using one or more of population density, population size or administrative division) for more than 100 countries, Ezatti et al.5 found that both body mass index (BMI) and blood cholesterol levels rose rapidly in tandem with increases in national income and level of urbanization. Work undertaken in Sri Lanka shows a greater increase in BMI and other risk factors for cardiovascular disease among urban dwellers than among their rural counterparts.6 Timely interventions in those stages of development in which environmental conditions shift and common modifiable risk factors emerge may help prevent and control chronic disease. It is important to identify these crucial stages and to determine what elements of urbanization are linked to the emergence of risk factors. A greater understanding of these relationships may help us identify interventions that are most likely to be effective in preventing NCDs in countries undergoing rapid urbanization and improve our capacity to stem the rapid increase in NCDs. The objectives of this study were to: establish the feasibility of collecting a multi-component scale of urbanicity in Tamil Nadu, India and, examine the relationships between urbanicity and chronic disease risk in Tamil Nadu, India. Methods The study was based on the working hypothesis that urbanicity, defined as the level of urbanization in a given locality,7 is as- sociated with risk factors for chronic disease. It was conducted in three steps: (i) constructing a measure of urbanicity using a validated scale8 based on data from the Census of India 2001 (ii) calculating the prevalence of NCD risk factors in seven study areas in the state of Tamil Nadu, India, by using data from an NCD risk factor surveillance survey conducted locally in 2003���2004 as part of a larger study9 and (iii) testing for an association between urbanicity and the prevalence of NCD risk factors in the study areas. Setting The urban arm of this study was set in Chennai (formerly Madras) and the rural arm was set in six settlements (Agaram, Une traduction en fran��ais de ce r��sum�� figure �� la fin de l���article. Al final del art��culo se facilita una traducci��n al espa��ol. .�������������� �������� ������������ �������� ���������� ���� �������������� �������� �������������� �������������� Objective To investigate the poorly understood relationship between the process of urbanization and noncommunicable diseases (NCDs) through the application of a quantitative measure of urbanicity. Methods We constructed a measure of the urban environment for seven areas using a seven-item scale based on data from the Census of India 2001 to develop an ���urbanicity��� scale. The scale was used in conjunction with data collected from 3705 participants in the World Health Organization���s 2003 STEPwise risk factor surveillance survey in Tamil Nadu, India, to analyse the relationship between the urban environment and major NCD risk factors. Linear and logistic regression models were constructed examining the relationship between urbanicity and chronic disease risk. Findings Among men, urbanicity was positively associated with smoking (odds ratio, OR: 3.54 95% confidence interval, CI: 2.4���5.1), body mass index (OR: 7.32 95% CI: 4.0���13.6), blood pressure (OR: 1.92 95% CI: 1.4���2.7) and low physical activity (OR: 3.26 95% CI: 2.5���4.3). Among women, urbanicity was positively associated with low physical activity (OR: 4.13 95% CI: 3.0���5.7) and high body mass index (OR: 6.48 95% CI: 4.6���9.2). In both sexes urbanicity was positively associated with the mean number of servings of fruit and vegetables consumed per day (P 0.05). Conclusion Urbanicity is associated with the prevalence of several NCD risk factors in Tamil Nadu, India. Level of urbanization and noncommunicable disease risk factors in Tamil Nadu, India Steven Allender,a Ben Lacey,a Premila Webster,a Mike Rayner,a Mohan Deepa,b Peter Scarborough,b Carukshi Arambepola,c Manjula Dattab & Viswanathan Mohanb a Department of Public Health, University of Oxford, Old Road, Oxford, OX3 7LF, England. b Madras Diabetes Research Foundation and Dr Mohan���s Diabetes Specialties Centre, WHO Collaborating Centre for Noncommunicable Diseases Prevention and Control, Gopalapuram, Chennai, India. c Department of Community Medicine, University of Colombo, Colombo, Sri Lanka. Correspondence to Steven Allender (steven.allender@dphpc.ox.ac.uk). (Submitted: 19 August 2009 ��� Revised version received: 21 September 2009 ��� Accepted: 28 September 2009 ��� Published online: 8 December 2009 )

Bull World Health Organ 2010 88:297���304 | doi:10.2471/BLT.09.065847 298 Steven Allender et al. Urbanization and noncommunicable disease risk in India Research Chunampet, Illeedu, Pudupattu, Puthi- ram Kottai and Vanniyanallur) in the Kancheepuram district, around 120 km south of Chennai. Measuring urbanicity An existing composite continuous mea- sure of urbanicity previously used and validated for the Philippines by Dahly and Adair8,10 was identified through an earlier systematic review.11 It comprised seven elements: population size, population density, access to markets, communica- tions, transport, education and health services. We replicated it in its entirety for three items ��� population size, population density and education ��� and modified the remaining four elements to better suit the Indian context. We assigned a maximum of 10 points to each item of the adapted scale, with a resulting range from 0 (no urbanicity) to 70 (high urbanicity) points. The scale is shown in Appendix A (available at: http://www.co-ops.net.au/ File.axd?id=1a5a5f74-6be2-4dc4-9f2d- 81319c0b8538). We conducted validity testing on the scale and obtained a Cronbach���s �� reliability coefficient of 0.72. Urbanicity as measured by this scale appeared to be consistent with a pre-existing govern- mental definition of ���urban��� and ���rural���. Results from a full validity study are in preparation. We took scale data from the 2001 Census of India, which had collected data on amenities in villages and towns throughout the country, including Tamil Nadu.12 It provided data for each admin- istrative area and considered Chennai to be a single administrative area. Data from the survey provided us with information on population size and density, education, health services and access to markets at each study location. As relevant data were collected by the amenities survey for rural, but not urban areas, we took communica- tions and transport data from the relevant municipal authority. Face validity for the scale was assessed using a photograph taken in the main street of each study location (Appendix B, available at: http://www.co-ops.net.au/ File.axd?id=22bb4a17-ca19-4cc5-87a0- e50ccee39d7b). After we quantified urbanicity, each location was classified into three urbanicity groups depending on its score: low (0 to 24), medium (24 to 46) and high (46���70). In this way, urbanicity was treated as an ordered categorical variable. Prevalence of NCD risk factors We took chronic disease risk factor data from the Indian NCD risk factor sur- veillance study carried out by the World Health Organization (WHO) and the Indian Council of Medical Research.9 The survey used the validated WHO STEPwise approach to Surveillance questionnaire13 and included information on tobacco use, diet (fruit and vegetable consumption), physical activity, weight and blood pressure. The questionnaire was pretested and modified to fit Indian conditions after pilot testing on a sub- population in each centre. Data were also collected on the age, sex, level of educa- tion and occupation of each participant. The survey also collected address and postcode information from participants and this information was used to link area-level data from the urbanicity scale to individual risk factor data. Biological and anthropological risk factors were assessed by measuring the height, weight and blood pressure of each participant. The BMI (kg/m2) was calcu- lated from the height and weight of each participant. Blood pressure was recorded in the right arm, in a sitting position, to the nearest 1 mmHg, using an electronic Omron blood pressure monitor. Two readings were taken 5 minutes apart and the mean of the two was taken as the blood pressure. Study sample groups For the WHO surveillance project, two samples were required: a rural sample of 2500 individuals and an urban sample of the same size. A 10% non-response rate was permitted. For the urban component, two of the 155 wards in Chennai were randomly chosen using computer-generated num- bers. Next, streets within the ward were randomly allocated using a computer programme and a house-to-house survey was conducted in each selected street until the urban sample of 2500 participants was achieved. For the rural component, a purposive sample of 33 villages in Kancheepuram district was undertaken to recruit the 2500 rural participants. Of the 33 villages in the original WHO study, six villages and one small rural town (Chunampet) were selected for our study, representing, for us, 1321 possible rural participants. These villages were selected to provide heterogeneity in, and data for, the urba- nicity scale. Participants were eligible if they were aged 15���64 years and had resided in the household for at least 6 months at the time of survey. When more than one individual in the household fulfilled the criteria, lots were drawn to select one participant per home. If the selected household did not have an eligible individual, the next household was contacted. Data on 3705 participants (97%) from the sample popu- lation (3821) were obtained, comprising a response rate of 86% within the rural sample and 100% within the urban sample. Participants were stratified by sex and 10-year age group from both urban and rural areas, with 250 participants in each stratum. Data were collected regardless of what day of the week it was. The study was undertaken based on the methods adapted from the WHO global STEPwise approach for NCD risk factor surveillance.13 Risk factor definition Tobacco use was defined as reported current daily smoking of tobacco low fruit and vegetable consumption as 5 servings of fruit and vegetables per day low physical activity as 150 minutes of moderate physical activity per week, high BMI as a BMI ��� 25 kg/m2 and high blood pressure as a systolic blood pressure ��� 140 mmHg and/or a diastolic blood pressure ��� 90 mmHg. Potential confounders and effect modifiers Preliminary analysis identified clear differences in risk factor prevalence be- tween men and women, so subsequent analysis was stratified by gender. Age is a well-established potential confounder in chronic disease studies, and crude gender-specific models were subsequently adjusted for age. Outcome variables Following WHO guidelines, we calculat- ed risk factor prevalence for each outcome within each study area. In addition, we calculated the mean number of servings of fruit and vegetables per day (based on the WHO STEPwise approach), mean time (minutes) spent in physical activ- ity per week, mean BMI, mean systolic blood pressure and mean diastolic blood pressure.13 Statistical methods Initial analysis produced descriptive statistics for each group. For continuous