text:
Factors associated with BMI, underweight, overweight, and obesity among adults in a population of rural south India: a cross-sectional study
- Matthew Little1Email author,
- Sally Humphries2,
- Kirit Patel3 and
- Cate Dewey1
DOI: 10.1186/s40608-016-0091-7
© Little et al. 2016
Received: 14 August 2015
Accepted: 8 February 2016
Published: 20 February 2016
Overweight, obesity, and related chronic diseases are becoming serious public health concerns in rural areas of India. Compounded with the existing issue of underweight, such concerns expose the double burden of disease and may put stress on rural healthcare. The purpose of this article was to present the prevalence and factors associated with underweight, overweight, and obesity in an area of rural south India.
During 2013 and 2014, a random sample of adults aged 20–80 years were selected for participation in a cross-sectional study that collected information on diet (using a food frequency questionnaire), physical activity (using the Global Physical Activity Questionnaire), socioeconomic position (using a wealth index), rurality (using the MSU rurality index), education, and a variety of descriptive factors. BMI was measured using standard techniques. Using a multivariate linear regression analysis and multivariate logistic regression analyses, we examined associations between BMI, overweight, obesity, and underweight, and all potential risk factors included in the survey.
Age and sex-adjusted prevalence of overweight, obesity class I, and obesity class II were 14.9, 16.1, and 3.3 % respectively. Prevalence of underweight was 22.7 %. The following variables were associated with higher BMI and/or increased odds of overweight, obesity class I, and/or obesity class II: Low physical activity, high wealth index, no livestock, low animal fat consumption, high n-6 polyunsaturated fat consumption, television ownership, time spent watching television, low rurality index, and high caste. The following variables were associated with increased odds of underweight: low wealth index, high rurality index, and low intake of n-6 PUFAs.
Underweight, overweight, and obesity are prevalent in rural regions of southern India, indicating a village-level dual burden. A variety of variables are associated with these conditions, including physical activity, socioeconomic position, rurality, television use, and diet. To address the both underweight and obesity, policymakers must simultaneously focus on encouraging positive behaviour through education and addressing society-level risk factors that inhibit individuals from achieving optimal health.
KeywordsUnderweight Overweight Obesity Physical activity Diet Rural India Tamil Nadu Diabetes
Obesity and its associated health effects are quickly becoming serious health concerns in low- and middle-income countries [1]. In 2010, the World Obesity Federation estimated that over one billion adults were overweight and 475 million were obese globally [2], the majority of which resided in developing countries in Latin America, Africa, and Asia. In India, successive studies suggest obesity is increasing rapidly, with recent prevalence estimates upwards of 15 % [3]. The sedentary and dietary effects of urbanization and modernization are often blamed for propagating overweight and obesity [4], however little research has examined the risk factors contributing to rising prevalence in rural areas where 70 % of the Indian population live and modernization has occurred less rapidly [5]. In-depth examinations of overweight and obesity in rural regions are therefore essential, especially considering these regions are often compromised by low literacy and poor access to healthcare services [6].
Overweight and obesity are associated with increased risk of non-communicable diseases such as metabolic syndrome, high cholesterol, type 2 diabetes mellitus, high blood pressure, and cardiovascular disease; conditions that are already serious public health concerns in rural and urban India alike [5, 7]. Studies of non-communicable disease biomarkers have long conferred the possibility of an 'Asian Indian phenotype' that produces higher-risk central adiposity at a lower body-mass index (BMI) than comparable populations in Europe and North America. For example, Razak et al. [8] found that average fasting glucose levels, LDL cholesterol concentrations, and blood pressure found in Europeans at a BMI of 30 kg/m2 could be found in Asian Indians at a much lower BMI. Such studies indicate that Asian Indians are more susceptible to the negative health consequences of overweight and obesity. Even more troubling is the co-occurrence of undernutrition and overnutrition in rural villages and households, termed the 'double burden' of disease, which serves to exacerbate poverty and limit economic growth in these areas [9]. While a number of studies have examined associations between dietary and lifestyle factors and prevalence of underweight, overweight, and obesity in the US [10, 11] and EU [12, 13], little research has examined similar associations in India, and even fewer in rural India [5].
The present study was conducted from November 2013 to March 2014 and examined a random sample of 753 people living in a rural region of northern Tamil Nadu. The objectives of the study were: (1) to determine the prevalence of underweight, overweight, and obesity among the study population; and (2) to determine the risk factors that affect BMI, underweight, overweight, obesity in rural regions of India using forwards stepwise linear and logistic regression models. The study was carried out in collaboration with the Development for Humane Action Network (DHAN) Foundation as part of a DFATD/IDRC-funded project entitled "Revalorizing small millets: Enhancing the food and nutritional security of women and children in rainfed regions of South Asia using underutilized species".
Ethics, consent, and permissions
We obtained clearance for the study from the University of Guelph Research Ethics Board (certificate reference number 12MY019). Permission for the study was granted by the High Commission of India in Ottawa, Canada. Upon arrival to the research site, and prior to the recruitment process, we approached local authorities (panchayat councils, local police officials, and hospital medical staff) and sought and obtained permission to carry out the study. Informed consent to participate in the study was obtained from all participants prior to their enrollment.
Sampling frame and recruitment
The sampling frame consisted of the entire adult population (>19 years old) of two rural panchayat wards (Anchetty panchayat and Madakkal panchayat), in the Krishnagiri District of Tamil Nadu. The region is comprised of several small villages surrounding the central market village of Anchetty.
Our target was to sample 800 participants following a sample size calculation for a sub-study published elsewhere [14]. A randomized two-stage recruitment method was employed, in which we first approached a random sample of 8 % of households in the sampling frame, then employed the WHO's Kish method to select a single household member for the study [15, 16]. If the selected individual refused, we removed them from the list of occupants and employed the Kish method again until the selected individual agreed to participate. If all adult members of the household were not present or refused, we moved to an immediately neighbouring household to recruit the required sample. All absences and refusals were considered non-responses in calculating response rate. We recorded the reason for non-response whenever possible. After securing oral consent to participate in the study, we organized a follow-up for completion of surveys and collection of health outcome data. One of either a doctor or a nurse collected anthropometric measurements and one of three nutritionists (one male, two female) conducted all interviews. Health care practitioners and nutritionists were gender-matched with participants to reduce potential response bias.
Anthropometric measurements and descriptive questionnaire
Standing height was measured at end of expiration against a flat wall using a ruler pressed against the crown of the head and a measuring tape. Weight was measured in light clothes with bare feet using a household digital scale (NOVA™ BGS1207 model). Blood pressure measurements were recorded as the average of two readings using an Omron™ BP786-10 handheld electronic blood pressure monitor in the sitting position using the right upper arm and one of three sized cuffs after a period of 5 min sitting. Participants completed a structured questionnaire about age, sex, occupation, education, medical history, tobacco use, socioeconomic status, physical activity, and dietary intake.
Socioeconomic status wealth index
We created a wealth index using a modified subset of 13 of 29 questions taken from the Standard of Living Index used by the 2nd round of the National Health and Family Survey [17]. We selected those questions we believed to be most relevant for our study population. They comprised both household and village characteristics, including: electrification (electricity, kerosene, gas or oil, other source of lighting), source of drinking water (pipe, hand pump, well in residence, public tap, hand pump, public well, or other water source), type of toilet facility (own flush toilet, public or shared flush toilet or own pit toilet, shared or public pit toilet, no facility), type of house (pucca, semi-pucca, or kutcha), cooking fuel (electricity, natural gas, kerosene, or wood). In addition, we collected information on ownership of house, land, livestock (cattle, chickens, goats, sheep, and buffalo), ceiling fan, radio, vehicle (bicycle, moped, motorcycle, or car), and television. Each attribute was weighted to give a maximum score of 36. Weights of items were developed by the International Institute of Population Sciences in India based on a priori knowledge about the relative significance of the items in determining SES [18]. Due to the rural subsistence nature of the local economy, this asset-based score is considered a more appropriate indicator of SES than education, income, or occupation [17]. For the remainder of the article, we will refer to this score as the 'wealth index'.
Rurality index
All previous epidemiology studies in India known to the authors define households as either rural or urban based on a dichotomous typology, which fails to adequately capture the variability inherent in the urban/rural continuum [19]. We created a rurality index to quantify the degree of rurality of each household. This index was based on that developed for the USA by Weinert and Boik (1995) and adapted for use in India [20]. The index was based on two factors: (1) distance to primary health centre, and (2) population of village in which household resides. We collected and standardized these values, then combined them, assigning a half weighted positive value to 'distance to healthcare facility' and a full weighted negative value to 'household population village'.
Physical activity assessment
We conducted the Global Physical Activity Questionnaire (GPAQ) with each participant, designed by the World Health Organization (WHO) as part of the WHO STEPwise approach to chronic disease risk factor surveillance [21]. GPAQ comprises 19 questions grouped to assess individuals' physical activity behaviour in three main domains over the course of 1 year: work, travel, and recreation. The GPAQ was paired with locally relevant photographs depicting 'moderate' and 'vigorous'-intensity work and recreation activities. In addition, we collected time spent sitting and time spent watching television per day. Physical activity scores were calculated using WHO's GPAQ Analysis Guide [21], which provided a total measure of Metabolic Equivalent (MET) minutes per week based on amount of time spent performing moderate (assigned 4 MET equivalents per minute) and vigorous physical activity (assigned 8 MET equivalents per minute) for work, transport, and recreation purposes. We scaled values to hours per day of moderate physical activity for easier interpretation.
Dietary and nutrition assessment
Diet was assessed using a south Indian food frequency questionnaire (FFQ), validated for Tamil Nadu by the Madras Diabetes Research Foundation (MDRF) [22]. MDRF provided permission to use the research tool and organized a 2-day training program for our three nutritionists on how to properly administer the FFQ, record responses, and enter data using their data entry software model. The FFQ uses a food atlas with photographs of dishes and serving sizes to collect information about the frequency of consumption of 212 dishes and foods. This tool is paired with EpiNu® [23], a software program that calculates overall intake of food categories and macro- and micronutrients based on frequency of consumption of dishes and laboratory-based nutritional analyses for each dish [24]. After completion of the study, FFQ data was analyzed by MDRF using EpiNu®, providing us with detailed data about dietary and nutrient intake for each participant. Energy intake was analyzed as kilocalories (kcal) consumed per day. All other dietary intake variables were scaled to grams per 1000 kcal consumed.
Literacy was defined as self-professed fluency in reading and writing. Tobacco consumers were defined as individuals who currently smoked at least one cigarette and/or chewed paan at least once per day. High blood pressure was defined as mean systolic blood pressure ≥ 140 mm Hg and/or mean diastolic blood pressure ≥ 90 mm Hg and/or treatment with blood pressure medication [25]. For each participant we calculated the body mass index (BMI) as the weight in kilograms divided by the squared height in meters (kg/m2).
As per the definitions of the International Obesity Task Force (IOTF), underweight was defined as BMI < 18.5 kg/m2, overweight was defined as BMI ≥ 23.0 kg/m2 but <25 kg/m2, obesity class I was defined as BMI ≥ 25 kg/m2 and < 30 kg/m2, and obesity class II was defined as BMI ≥ 30 kg/m2 [26]. Low caste was analyzed as a binary variable with Scheduled Castes (SC) and Scheduled Tribes (ST) being low caste and all others as an amalgamated referent. High caste was analyzed as a binary variable, with Brahmins being high caste and all other castes as an amalgamated referent.
Statistical analysis
We identified outliers, cleaned the data set, and completed summary statistics in Microsoft Excel 10.0 [27]. No values were considered outliers after correcting data entry errors. As such, all observations were included in the analysis.
Underweight, overweight, and obesity prevalence data were standardized using state-level age and sex data from the 2011 national census [28]. In separate models, univariate associations between sex and BMI category were analyzed. Since there were no significant associations between sex and outcome variables, we present the results for men and women combined. Means of descriptive characteristics, socioeconomic and education characteristics, physical activity habits, and dietary intake were calculated across categories of BMI, including underweight, normal, overweight, obesity class I, and obesity class II. Values were expressed as mean +/- SD or percentages. One-way analysis of variance (for continuous variables) and Pearson's X 2 test were used to examine differences across outcome groups. Following this, three multivariate regression models were built in STATA 13.0 [29].
Model one
We employed a forward stepwise linear regression model-building process to determine associations between BMI as a continuous variable and putative risk factors. All factors were first analyzed in univariate regressions (see Appendix 1 for full list of factors). Variables with significant associations (p-value < 0.2) in univariate analyses were considered for an initial multivariate linear regression model. We then employed a forward stepwise model building process and eliminated non-significant variables (using a p-value cut-off of 0.05) from the multivariate model, assuming a lack of confounding if coefficients of all remaining variables did not change by more than 20 % after addition or removal of the potential confounder [30]. Quadratic terms and interaction terms were assessed if there were biological or practical reasons to believe they may be significant. BMI was log-transformed to improve normality of residuals and homoscedasticity. We ensured the transformed model obeyed the assumptions of linear regression models, including independence of variables, normality of residuals (using a Shapiro-Wilks test and quartile quartile plots), and homoscedasticity (using a Cook-Weisberg test and a residual-fitted value plot).
Model two
In a second step, we fit a multinomial logistic regression with overweight, class I obesity, and class II obesity as separate outcome groups. Underweight and normal weight classes were combined into a referent category to increase the statistical power of the analysis [31]. We examined the same variables as those assessed in the linear regression analysis (Appendix 1). Variables were first assessed for significance in multinomial univariate logistic analyses. All variables with associations (p-value <0.2) with any of the outcome groups were included in the initial multivariate analysis. We then employed a forward stepwise process and eliminated non-significant variables (p-value <0.05) using the same methods as those used in model one to determine adjusted odds ratios (ORs) and 95 % confidence intervals (CIs).
Model three
In a final step, we fit a multivariate logistic regression with underweight as an outcome group. All other weight categories were combined as the referent group [32]. We examined the same variables as those assessed in the models one and two (see Appendix 1). We then followed the same methods as used in model two in order to determine adjusted ORs and corresponding CIs for each significant variable (p-value less than 0.05) in the final model.
Of the 812 individuals recruited for the study, 753 (92.7 %) participated (341 men and 412 women), of whom 752 (92.6 %) completed an FFQ and 745 (91.7 %) consented to anthropometric measurements. Response rate was 87.4 % among men and 99.2 % among women. Disparity in the response rate was primarily due to migration among local men and thus unavailability at the time of sampling. The mean age was 47 ± 14.7 and the literacy rate was 35.1 %. Crude prevalence rates of underweight, overweight, obesity class I, and obesity class II were 22.7, 15.8, 17.7, and 3.5 %, respectively. Age- and sex-adjusted prevalence of underweight, overweight, obesity class I, and obesity class II were 22.7, 14.9, 16.1, and 3.3 % respectively.
Baseline characteristics of a sample of individuals in rural south India by BMI category
Underweight (BMI < 18.5 kg/m2, n = 84)
Normal (n = 300)
Overweight (BMI ≥ 23.0 kg/m2 and <25 kg/m2, n = 118)
Obesity class I (BMI ≥ 25 kg/m2 and < 30 kg/m2, n = 132)
Obesity class II (BMI ≥ 30 kg/m2, n = 26)
p-value for trend
Descriptive characteristics
49.6 ± 17.2
46.8 ± 14.8
47.4 ± 13.5
45.1 ± 11.7
43.6 ± 11.5
Women (%)
Waist to hip ratio
0.84 ± 0.072
0.87 ± 0.075
0.91 ± 0.07
0.92 ± 0.09
0.92 ± 0.08
Hypertension (%)
19.4 %
Rurality Index
0.04 ± 1.1
−0.37 ± 1.3
−0.86 ± 1.32
−1.0 ± 1.28
−1.95 ± 1.0
Current tobacco consumer (%)
Socioeconomic and education characteristics
Wealth index
9.7 ± 3.9
10.7 ± 4.6
11.3 ± 4.8
12.3 ± 5.0
13.5 ± 5.6
Vehicle ownership (%)
Television ownership (%)
Pucca housing (%)
In-house tap water (%)
Education (grade achieved)
2.1 ± 3.5
3.1 ± 4.4
3.2 ± 4.2
3.7 ± 4.5
4.4 ± 4.8
Physical activity characteristics
Physical activity (hours/day of moderate physical activity)
4.0 ± 3.4
4.4 ± 3.7
4.3 ± 4.0
3.2 ± 3.4
1.1 ± 2.3
Sitting time (hours/day)
4.2 ± 2.6
4.3 ± 4.3
4.3 ± 2.5
5.0 ± 2.8
6.2 ± 2.8
Television time (hours/day)
1.3 ± 1.35
1.3 ± 1.2
1.6 ± 1.2
1.8 ± 1.37
2.1 ± 1.6
Labour occupation (%)
Dietary characteristics
Current alcohol consumer (%)
Total energy intake (kcal/day)
2390 ± 758
2365 ± 724
2439 ± 625
2440 ± 702
2031 ± 585
Carbohydrates (g/1000 kcal)
183 ± 14
181 ± 15
177 ± 15
177 ± 13
170 ± 8
Protein (g/1000 kcal)
25.5 ± 1.9
25.7 ± 2.0
25.5 ± 1.8
25.9 ± 1.7
26.2 ± 1.8
Total fat (g/1000 kcal)
18.2 ± 5.3
19.2 ± 5.3
20.6 ± 4.8
21.2 ± 5.1
23.6 ± 2.9
Dietary fibre (g/1000 kcal)
34.4 ± 12
32.7 ± 13
29.3 ± 11
29.6 ± 11
23.1 ± 7.7
Dairy products (g/1000 kcal)
85.2 ± 77
74.1 ± 64
79.7 ± 62
87.1 ± 67
102.5 ± 66
Pulses and legumes (g/1000 kcal)
24.2 ± 11
27.6 ± 12
29.6 ± 12
29.4 ± 12
30.7 ± 11
Meat and poultry (g/100 kcal)
2.93 ± 3.8
3.3 ± 4.1
2.8 ± 3
3.3 ± 4.6
3.7 ± 3.4
Fruits and vegetables (g/1000 kcal)
63.1 ± 41
75.6 ± 46
86.6 ± 58
84.3 ± 50
87.0 ± 37
Refined grains (g/1000 kcal)
135 ± 76
153 ± 83
178 ± 90
165 ± 75
154.6 ± 55
Model one: linear regression of BMI
Factors associated with body mass index for a sample of individuals living in rural south India based on a multivariable analysis
Standard error
P value
Physical activity (h/day of moderate activity)
Wealth index
Livestock ownership (Y/N)
Animal fats consumption (g/1000 kcal)
n-6 PUFA consumption (g/1000 kcal)
Television time (h/day)
Tobacco consumption (Y/N)
MSU rurality index
High caste (Y/N)
Model two: multinomial logistic regression of overweight and obesity
Factors associated with overweight, class I and class II obese for a sample of individuals in rural south India
Risk factor
Overweight (23–25 kg/m2) OR 95 % CI
Obesity class I (25–30 kg/m2) OR 95 % CI
Obesity class II (>30 kg/m2) OR 95 % CI
Physical activity (h/day of moderate activity)
1.02 (0.96, 1.08)
0.94b (0.88, 0.99)
0.75a (0.62, 0.92)
Wealth index
1.02 (0.97, 1.07)
1.07a (1.03, 1.12)
1.09b (1.00, 1.17)
Own a television (Y/N)
2.88b (1.08, 7.67)
2.19 (0.81, 5.87)
1.17 (0.13, 10.8)
Television time (h/day)
1.06 (0.90,1.26)
1.19b (1.01, 1.39)
1.22c (0.13, 10.8)
High caste (Y/N)
2.37b (1.21, 4.67)
3.54a (1.90, 6.60)
4.62b (1.22, 17.49)
Rurality index
0.70a (0.52, 0.83)
0.69a (0.59, 0.82)
0.41a (0.26, 0.63)
Tobacco consumption
0.80 (0.52, 1.23)
0.75c (0.48, 1.16)
0.15b (0.035, 0.68)
Model three: multinomial logistic regression of underweight
Factors associated with underweight for a sample of individuals in rural south India
Risk factor
Odds Ratio (95 % CI)
Wealth index
0.93a (0.90, 0.98)
Rurality index
1.48a (1.24, 1.56)
n-6 PUFA consumption (g/1000 kcal)
0.98b (0.95, 0.99)
Tobacco consumption
0.77a (0.43, 1.38)
Age and sex-adjusted prevalence of underweight, overweight, obesity class I, and obesity class II were 22.7, 14.9, 16.1, and 3.3 %, respectively. Prevalence rates of underweight, overweight, and obesity class I were not significantly different between women and men. Obesity class II was more common among women (p < 0.05), a finding that is consistent with several other studies in India [34, 35]. Age and sex-adjusted prevalence of underweight was lower than the latest (2005) national rural estimate of 35 %, indicating less undernutrition in the study site than elsewhere in India [3]. Age and sex-standardized prevalence of obesity was higher than the latest national rural estimate of 6 % [34], which is likely partially due to different BMI cut-off values and age differences in the sample population. Prevalence rates of overweight and obesity were comparable to more recent studies in rural south India that used similar cut-off values and age categories [34, 36]. For example, Misra et al. (2011) found prevalence of overweight and obese were 14 and 18 % in rural Tamil Nadu [35].
The simultaneous high prevalence of underweight and overweight/obesity indicates continued existence of the 'double burden of disease' as described by Doak et al. (2005) [8]. While we are unable, due to sampling methods, to determine if underweight and overweight persons cohabit the same households, our findings do indicate the coexistence of underweight and overweight at the village level. Similar results have been found in agricultural regions of Brazil [37], South Africa [38], Russia [39], and China [40]. With age and sex-standardized prevalence rates of underweight and overweight/obesity accounting for over half the study population, and the potential for future increases in prevalence of overweight/obese as well as complications due to associated non-communicable diseases, this double burden should be considered a major concern that threatens to strain the limited health care services in the region.
Correlates of underweight, overweight, and obesity
While most studies examining risk factors contributing to underweight, overweight, or obesity categorize or dichotomize BMI and analyze associations using logistic regressions (e.g. [6, 12, 13, 41]), there is a large body of literature suggesting that categorizing continuous variables isn't statistically effective as it results in a loss of information and power due to a reduction of variability in each category [42–44]. For this reason, we conducted linear regressions in addition to logistic regressions. We found strong independent associations between several risk factors and BMI, underweight, overweight, and obesity.
Physical activity
In the unadjusted univariate model, each hour of moderate physical activity was associated with a 0.095 kg/m2 decrease in BMI. After adjusting for other variables, each hour of moderate physical activity was associated with a 0.085 kg/m2 decrease in BMI. This is equivalent to -231 g for a typical Indian man (165 cm tall) and -196 g for a typical Indian woman (152 cm tall) (based on height data from Mamidi et al. 2011 [45]). Although the magnitude of this difference may seem small, when the coefficient was applied to the values for low (10th percentile of PA scores) versus high (90th percentile), the model predicted a difference of -2.34 kg for a typical man and -1.99 kg for a typical woman. In addition, higher physical activity was associated with lesser odds of obesity class I and obesity class II. These findings support the ideas raised by Shetty et al. (2002) that reduced physical activity is one of the main causes of overweight and obesity in India [46]. Due to the cross-sectional nature of the study, we are unable to determine if reduced physical activity is a cause or a consequence of weight gain [27, 47]. Physical activity was not associated with underweight despite suggestions by Hausenblas and Downs that excessive exercise (e.g. forced labour) could lead to underweight and wasting [48, 49]. However, it is unlikely that such individuals were captured in the study.
Several researchers suggest that modernization has contributed to declining physical activity in a number of ways. An influx of vehicles has reduced the need for active transportation [5]. Mechanization of farming processes has resulted in less need for strenuous labour. For example, Ramachandran et al. (2004) found a 57.2 % decrease in the number of individuals engaged in manual labour in a rural population of eastern Tamil Nadu between 1989 and 2003 [50]. Exercise for leisure is not common in rural India, so reduced physical activity for work or travel is rarely offset by recreational activities [51]. Taking into account the growing prevalence of overweight and obesity in India, our findings are consistent with the view that physical activity may reduce or prevent increases in body weight. Our results suggest that even a modest level of activity may confer health benefits.
Rurality index
The Census Bureau of India classifies households as either urban or rural and policymakers enforce these definitions in health, food, and economic policies [52]. Researchers in India have adopted this definition when examining the effects of urbanization on chronic disease outcomes, often making comparisons between rural and urban populations [35, 53–56]. However, a dichotomous definition of rurality is ineffective when examining rural or urban populations separately. In addition, it fails to account for the gradient of rurality, homogenizes a heterogeneous population, and oversimplifies the effects of rurality as a descriptive factor [57]. Indeed, as stated by Rousseau (2005), 'rural' should not imply a single community but a wide range of communities with various features [58]. A standardized definition of rurality has thus proved elusive, as the essence of 'rural' villages is a complex and context-specific interplay of culture, affluence, geography, agriculture, and access to markets. Several studies have confirmed that urban status is associated with higher BMI and greater odds of overweight and obesity and rural status is associated with greater odds of underweight [35, 53–55]. Upon deeper exploration, many of these studies described associations between urban or rural status and other more proximate risk factors, such as income, physical activity, and diet (e.g. Pandey et al. 2013 [54]). While in our study, the entire study region is classified as rural, we were interested in the more nuanced conceptualization of a rural–urban continuum [59, 60]. In light of the many public health problems posed by urbanization, we believe it prudent to quantify and examine this concept in an epidemiologic capacity.
Researchers in the United States [20], Canada [61, 62], Australia [63], and Britain [64] have developed localized rurality indices intended to quantify the rural–urban continuum for researchers, policymakers, and healthcare providers. However, no rurality index has been developed for India. We elected to adapt the Montana State University (MSU) rurality index [20] due to its simplicity and relevance to the Indian context. Our results showed greater rurality was associated with lower BMI and decreased odds of overweight, class I obesity, and class II obesity. In addition, increased rurality was associated with greater odds of underweight. We therefore posit that degree of rurality is accompanied by a gradient of lifestyle and dietary differences not captured by other measures. In addition, individuals living in larger villages and closer to the market may experience processes associated with urbanization while still maintaining their rural status as per the Indian definition. Some studies have suggested urban residents face greater stress loads and higher prevalence of depression, which in turn affects physical health and possibly BMI [65, 66]. As we did not collect or analyze any measure of stress, perhaps the effects of such 'hidden' risk factors are captured by the rurality index. Further research on rurality and health outcomes is crucial to encourage policymakers to reevaluate the simplistic rural/urban dichotomy upon which most health, food, and economic policy is founded.
Socioeconomic status (SES)
The asset-based wealth index, analyzed as a proxy for SES, was positively associated with BMI and greater odds of obesity class I and obesity class II. Low wealth index was also associated with increased odds of underweight. Considering the large variance of the wealth index (our values ranged from one to 26), the magnitude of differences between predicted values of BMI is large. When the coefficient was applied to the values for the 10th and 90th percentile of wealth index values, the model predicted a difference of 4.57 kg for a typical man and 3.88 kg for a typical woman.
It is likely that SES acts is a distal factor and the exact mechanisms through which it may affect odds of underweight, overweight, and obesity are varied [67]. Families with higher socioeconomic status likely differ in their lifestyle—including dietary and physical activity patterns—thus affecting risk. While we examined these factors, the wealth index may have captured excess variability not completely accounted for by our assessments. Associations between socioeconomic markers (education, income, and possession-based wealth indices) and obesity have been found in other recent studies of rural individuals [6, 68]. Those of lower SES were more likely to be underweight, a result that is consistent with other studies in rural Tamil Nadu [69] and elsewhere in India [6]. This finding corresponds with the large body of research suggesting low socioeconomic status is associated with limited food intake and excessive manual labour, thus making it difficult for individuals to achieve net positive energy intake required to maintain or gain weight [6, 19, 34]. Such associations support the theory that social determinants play a role in both over- and undernutrition [70].
Livestock ownership
A unique finding of the present study was the inverse association between livestock ownership and BMI. Although livestock ownership was included in the wealth index, it was also independently associated with lower BMI when controlling for extemporaneous variables. Due to the lack of formal research on this topic, our knowledge of this association is limited to conjecture. In rural Tamil Nadu, most farmers graze their animals on forested public lands [71, 72], which often leads to individuals walking long distances every day to access fodder for their livestock. The result may be higher energy expenditure not fully captured by the PA score. Livestock ownership may also increase accessibility to milk products and lean meats such as chicken and mutton, thus resulting in increased consumption of animal proteins not fully captured in the FFQ. Regardless, this association was unexpected and merits further investigation on a larger scale.
Television ownership and TV time
Prevalence of television ownership was very high at 88.1 %. Although comparative data from other studies in India are rare, we found higher ownership rates than those reported by the 2006 National Readership Study, which found televisions in 76.2 % of households in Tamil Nadu [73]. Our findings are likely attributable to a populist scheme launched by the ruling Tamil Nadu state political party in 2006 to distribute colour televisions to the poor [74]. Over the following 4 years, the government purportedly delivered over 13.7 million televisions to Tamil Nadu residents [75].
Television ownership was associated with higher odds of overweight and class I obesity. This association is likely two-fold. First, television ownership may be indicative of overall wealth and SES. Although it was included in the wealth index, due to high prevalence of television ownership (Table 1) and its relatively low weight in the aggregate score, it may have had a small impact on wealth index and thus is also independently indicative of SES. This may indicate that some poor families were overlooked by the free TV scheme, or sold the televisions they received. Second, television ownership may encourage sedentary leisure time. Indeed, we found that time spent watching television ("TV time") was associated with higher BMI and increased odds of class I obesity. TV time has been linked with overweight and obesity among children in other areas of India, however this is the first study to find such an association among adults [76]. Surprisingly, sitting time was not significantly associated with any outcome, contrary to findings in other countries [77]. It is possible that television ownership and TV time are more accurate indicators of sedentary leisure time than reported sitting time due to response bias.
High caste
High caste was associated with higher BMI and increased odds of obesity class I. These results correspond with those of Gaiha et al. (2010) [69], who found that low caste (including SC, ST, and other backwards caste (OBC)) was associated with lower risk of obesity. Similarly, Adinatesh et al. (2013) [78] found a higher prevalence of overweight and obesity among higher-caste individuals when compared to those identifying as SC, ST, or OBC. This association is likely complex, but we conjecture that it is likely due to two main categories of factors. The first is lifestyle factors. As seen by Adinatesh et al., high caste populations tend to have lower undernutrition, higher income and standards of living, greater access of sedentary pastimes (e.g. television, video games), and increased usage of vehicles for transportation. In addition, geneticists have recently proven the existence of genetic differences between castes, with higher castes having a higher proportion of West Eurasian genetic admixture [79]. Unfortunately, no research has yet examined caste genetics as they pertain to health and disease outcomes. Considering the roles of genetic factors in development of overweight and obesity [80], genetic differences between castes may play an important role in the association between caste and BMI. More research is needed to explore the ongoing role of caste and caste genetics in determining risk of overweight and obesity in rural areas.
Tobacco consumption
In India, tobacco is consumed in various forms. Cigarettes are growing in popularity, especially among urban and high-income populations [81]. Bidis (thin hand-rolled tobacco leafs) are cheaper and more commonly smoked among lower-income and rural populations. In addition, use of smokeless tobacco is pervasive among both men and women. Smokeless tobacco is often consumed as a combination of betel-leaf, areca nut, and tobacco called paan [82]. We found that any-type tobacco consumption was associated with lower BMI and lower odds of obesity class II. Tobacco use was also associated with increased odds of underweight. These findings are consistent with previous studies in the US [83–85] and urban India [86]. While the reasons behind this association are varied and complex, it is likely that tobacco may act directly (by affecting appetite and other aspects of physiology) or indirectly (by decreasing the purchasing power for food and therefore quality of diet) [82]. Considering the implications of tobacco consumption on both risk of underweight and risk of cancers and cardiovascular diseases, public health programs must emphasize preventing tobacco use and supporting cessation efforts among addicted individuals.
Dietary factors
India is experiencing a nutrition transition characterized by decreased consumption of coarse cereals and dietary diversification [44]. In some ways, this has benefited rural nutrition; data from the past 40 years suggest that fruit and vegetable consumption has doubled and overall protein intake has improved [4]. However, there have been significant drawbacks. Traditional small millets have been replaced by refined wheat and polished white rice due to imbalanced subsidization and shifting taste preferences [87]. Consumption of fats and sugars is also increasing due to the rising popularity of snack foods. We examined intake of foods, food groups, macro-nutrients, and micro-nutrients for potential associations with BMI, overweight, and obesity. We found significant associations with dietary intake of n-6 polyunsaturated fatty acids and animal fats.
Oil consumption
The role of dietary fats in adult human obesity is a controversial issue [88]. A review by Willett (2001) [89] concludes that diets high in fat do not account for high prevalence of excess body fat in Western countries, and that the emphasis on total fat reduction has been a "serious distraction in efforts to control obesity and improve health in general" (pp. 59). His argument is based on both epidemiological evidence showing a poor association between total fat intake and obesity, and an assessment of published clinical trials that examine changes in fat composition in the diet. By contrast, a separate review conducted by Bray and Popkin (1998) [90] concludes that fat intake does promote obesity through passive overconsumption. Animal studies show a strong association between high-fat diets and obesity, and some studies on humans found that increasing fat intake was associated with an increase in BMI and higher odds of obesity [91, 92]. Bray and Popkin conclude that a reduction in dietary fat should be seen as a means to reduce to reduce total energy intake and reduce the energy density of the diet. The debate is inconclusive and as yet, there is no consensus in the literature about the association between fat intake and BMI, overweight, and obesity.
Evidence from the present study suggests that such reviews may disregard the qualitative composition of fat intake and the role of different types of fats. Intake of n-6 (also called omega-6) polyunsaturated fatty acids (PUFAs) was positively associated with BMI and negatively associated with odds of underweight. Ailhaud et al. (2006) claim that increased consumption of n-6 PUFAs, especially when combined with low intake of n-3 PUFAs, may promote development of excessive adipose tissue [86]. One clinical trial found that in elderly men fed a diet high in linoleic acid (LA, a n-6 PUFA), the mean body weight of the experimental group (n = 393) increased while the mean body weight of the control group (n = 389) decreased. Other studies have found that higher n-6 PUFA to n-3 PUFA intake ratios result in increased risk of several chronic diseases, including coronary heart disease, hypertension, type 2 diabetes, renal disease, rheumatoid arthritis, ulcerative colitis, Crohn's disease, and chronic obstructive pulmonary disease, many of which have independent associations with adiposity [93]. Other beneficial effects of n-3 PUFAs include improvement of platelet aggregation, serum triglyceride concentrations, and anti-arrythmic effects. Some of these effects are directly related to reduced metabolic syndrome and obesity. Despite this, and despite independent connections between the metabolic syndrome and excess adiposity, the role of n-6 fatty acid intake in overweight and obesity is not confirmed nor well understood. Clearly, further studies are needed.
In India, n-6 PUFA intake has increased in recent years, especially in landlocked areas with little access to seafood [46]. While many studies recommend a n-6:n-3 ratio of at least 4:1 [93], the mean n-6:n-3 ratio in our study population was 24:1. Although n-6:n-3 intake ratio was not significantly associated with BMI or any outcome variables, our results indicate that n-6 intake may be associated with body weight in rural India. Considering the high intake of n-6 PUFAs in comparison to n-3 PUFAs and the wealth of data indicating the health benefits of n-3 PUFAs, food policies and health education programming aimed at increasing consumption of n-3 PUFAs would be beneficial. Such actions may also reduce prevalence of underweight by discouraging energy-dense and nutrition-poor diets in favor of those that are nutrition-dense and energy-adequate [94]
Animal fat intake
We found animal fat intake (saturated fats in milk products and meat) was negatively associated with BMI. This trend conflicts with animal experiments showing that diets high in animal fats leads to obesity and insulin resistance [95]. Less research has been done in this area with humans. In adults, failing to control for factors such as physical activity or smoking often confounds studies. Some epidemiologic cross-sectional studies found associations between saturated fatty acid intake and BMI or other obesity outcomes [96–98]. Another cross-sectional epidemiology study found that obese children had higher intake of saturated fatty acids after controlling for physical activity and overall energy intake [99]. However, due to the cross-sectional nature and low sample size of these studies, it is difficult to reach any causative conclusions about the effects of animal fat intake on overweight and obesity. Therefore, although our results conflict with other studies, they also add to this debate and we encourage further research with larger sample sizes before making any broad conclusions about the effects of dietary fats on adiposity. We may suggest that certain authors, such as Gaiha et al. (2010) [69] are too hasty in their recommendations to limit saturated fats in favour of polyunsaturated fats.
This is the first cross-sectional epidemiological study, to our knowledge, to find significant linear associations between fatty acid intake and BMI while controlling for many extemporaneous risk factors. However, we found no significant association between n-6 PUFA and animal fat consumption and overweight, obesity class I, or obesity class II during the logistic regression analysis, while n-6 PUFA intake was associated with decreased odds of underweight. This may indicate that associations between fatty acid intake and BMI are driven by values within the underweight and normal range. In addition, average fat intake of the study population was well below values reported elsewhere in India [22] and also below recommendations for rural populations set out by the Indian National Institute of Nutrition [100], which may have skewed results. Further research is therefore needed in this field.
Our study has a number of limitations. Most importantly, the cross-sectional nature of the study precludes the ability to distinguish causes from effects. BMI was used as a measure of adiposity instead of waist-to-hip ratio or triceps or subscapular skinfold thickness, which may be more valid methods of determining excess body fat and categorizing high-risk overweight and obesity [101]. However, BMI is more accurate when comparing individuals within the same ethnic group [101]. The choice of newer and more sensitive IOTF BMI cut-offs for determining overweight and obesity reduces the comparability of our data to previous studies in India. Several potential risk factors (e.g. family history of obesity, early childhood adiposity) were not assessed or controlled for since these data were not available. An FFQ was used for dietary assessments, and the validity of this approach has been documented by comparisons with more detailed methods [22]. However, FFQs are imperfect, are subject to recall bias, and do not reflect long-term dietary changes that may impact adiposity and disease risk [102].
The epidemic of overweight and obesity is a serious public health issue in rural India and raises concerns about the capacity of the health care system to cope with associated outcomes. Alarming prevalence data, including those in the current study, provide a dire picture and a call to action. A population-level strategy to prevent obesity is necessary. Such a strategy requires sound data to reduce inefficiencies and target risk factors that have the greatest impact. While further research is needed, results from our study suggest several risk factors that are associated with BMI, overweight, and obesity, and may inform public health programming. Risk factors identified by our study may be divided into two categories. One category is individual-level risk factors that may be targeted by behaviour changing programming. Such factors included physical activity, television usage, and dietary factors such as finger millet, oil, and fat consumption. The second category comprises society-level risk factors such as caste and socioeconomic status. These risk factors must be addressed on a macro- level through social welfare policy, public infrastructure and development programs. As India continues to develop, modernize, and urbanize, there needs to be a strong education program to ensure that populations are aware of the dangers of overweight, obesity, and related noncommunicable diseases.
body mass index
confidence interval
European Union
food frequency questionnaire
global physical questionnaire
Madras Diabetes Research Foundation
other backwards castes
odds ratio
physical activity
polyunsaturated fatty acid
scheduled caste
socioeconomic status
scheduled tribe
United States
World Health Organization
The authors would like to acknowledge the International Development Research Centre (IDRC) and the Department of Foreign Affairs, Trade, and Development for their financial support through the Canadian International Food Security Research Fund. The IDRC also provided financial support through a doctoral research fellowship. The Development for Humane Action Foundation provided valuable field assistance and offered a local perspective on all research. Particular thanks are owed to project coordinator M. Karthikeyan, whose assistance was greatly appreciated. The local research team comprised of several very talented and capable individuals, including T. Madhe Gowda, Priscilla Dhanapal, N. Kodhai Priya, Dr. Vikas Kumar, and T. Rajesh. Funding throughout the data analyses and writing processes was provided by the Canadian Institutes of Health Research through the Vanier Scholars program.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors' Affiliations
- Fuster V, Kelly BB, editors. Promoting Cardiovascular Health in the DevelopingWorld: A Critical Challenge to Achieve Global Health. Washington, DC: National Academies Press; 2010.Google Scholar
- World Health Organization. Obesity and overweight fact sheet. IOTF report. 2015. Available at http://www.who.int/mediacentre/factsheets/fs311/en/. Accessed 10 February 2015.
- International Institute for Population Sciences (IIPS). National Family Health Survey (NFHS-3). Ministry of Health and Family Welfare, Government of India. 2007. http://dhsprogram.com/pubs/pdf/frind3/00frontmatter00.pdf. Accessed 15 Jan 2015.
- Misra A, Singhal N, Sivakumar B, Bhagat N, Jaiswal A, Khurana L. Nutrition transition in India: Secular trends in dietary intake and their relationship to diet-related non-communicable disease. J of Diab. 2011;3(4):278–92.View ArticleGoogle Scholar
- Misra P, Upadhyay R, Misra A, Anand K. Review of the epidemiology of diabetes in rural India. J Diab Res Clin Pr. 2011;92(3):303–11.View ArticleGoogle Scholar
- Zaman M, Patel A, Jan S, Hillis GS, Krishnam Raju PP, Neal B, Chow CK. Socio-economic distribution of cardiovascular risk factors and knowledge in rural India. Int J Epidemiol. 2012;41:302–14.
- Mahadik SR, Deo SS, Mehtalia SD. Increased prevalence of metabolic syndrome in non-obese asian Indian-an urban-rural comparison. Metab Syndr Relat Disord. 2007;5:142–52.View ArticlePubMedGoogle Scholar
- Razak F, Anand SS, Shannon H, Vuksan V, Davis B, Jacobs R, Teo KK, McQueen M, Yusuf S. Defining obesity cut points in a multiethnic population. Circulation. 2007;115:2111–8.
- Doak CM, Adair LS, Bentley M, Monteiro C, Popkin BM. The dual burden household and the nutrition transition paradox. Int J Obes. 2005;29(1):129–36.View ArticleGoogle Scholar
- Danaei G, Ding EL, Mozaffarian D, Taylor B, Rehm J, Murray CJL, et al. The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Med. 2009;6:e1000058.PubMed CentralView ArticlePubMedGoogle Scholar
- Ewing R, Shmid T, Killingsworth R, Zlot A, Raudenbush S. Relationship between urban sprawl and physical activity, obesity, and morbidity. Am J Health Promot. 2003;18(1):47–57.View ArticlePubMedGoogle Scholar
- Kleiser C, Rosario AS, Mensink GBM, Prinz-Langenohl R, Kurth BM. Potential determinants of obesity among children and adolescents in Germany: results from the cross-sectional KiGGS study. BMC Public Health. 2009;9:46–54.PubMed CentralView ArticlePubMedGoogle Scholar
- Janssen I, Katzmarzyk PT, Boyce WF, Vereecken C, Mulvihill C, Roberts C, Currie C, et al. Comparison of overweight and obesity prevalence in school-aged youth from 34 countries and their relationship with physical activity and dietary patterns. Obes Rev. 2005;6:123–32.
- Little M, Humphries S, Patel K, Dodd W, Dewey C. Factors associated with glucose tolerance, pre-diabetes, and type 2 diabetes in a rural community of south India: A cross sectional study. Diabetology and Metabolic Syndrome. 2016. In Press.
- Karthikeyan M, Jena D, Patel K, Khadka K, Devkota R, Samaratunga H, et al. Baseline Survey in Project Sites: A Report as a Part of Revalorizing Small Millets in the Rainfed Regions of South Asia (RESMISA). India, Nepal, Sri Lanka and Canada: DHAN Foundation, Arthacharya Foundation, Local Initiatives for Biodiversity, Research and Development (LIBIRD) and Canadian Mennonite University; 2012.Google Scholar
- World Health Organization. WHO STEPS Surveillance Manual: The WHO STEPwise approach to chronic disease risk factor surveillance. 2005. http://apps.who.int/iris/bitstream/10665/43376/1/9241593830_eng.pdf?ua=1. Accessed 10 Dec 2014.
- International Institute for Population Sciences (IIPS). National Family Health Survey (NFHS-2). Mumbai: IIPS; 2007.Google Scholar
- Ebrahim S, Kinra S, Bowen L, Andersen E, Ben-Shlomo Y, Lyngdoh T, Ramakrishnan L, et al. The effect of rural-to-urban migration on obesity and diabetes in India: A cross-sectional study. PLoS Med. 2007;7(4):e1000268.
- Humphreys JS. Delimiting 'rural': implications of an agreed 'rurality' index for healthcare planning and resource allocation. Aust J Rural Health. 1998;6(4):212–6.View ArticlePubMedGoogle Scholar
- Weinert C, Boik RJ. MSU Rurality index: development and evaluation. Res Nursing Health. 1995;18:453–64.View ArticleGoogle Scholar
- World Health Organization. Global Physical Activity Questionnaire (GPAQ) Analysis Guide. 2011. http://www.who.int/chp/steps/resources/GPAQ_Analysis_Guide.pdf. Accessed 10 November 2013.
- Sudha VG, Sathya RM, Ganesan A, Mohan V. Reproducibility and validity of an interviewer-administered semi-quantitative questionnaire to assess dietary intake of urban adults in southern India. Int J Food Sci Nutr. 2006;57(7-8):481–93.View ArticlePubMedGoogle Scholar
- Madras Diabetes Research Foundation. EpiNu® version 1.0. Chennai, India; 2007.
- Radhika G, Sathya RM, Ganesan A, Saroja R, Vijayalakshmi P, Sudha V, et al. Dietary profile of urban adult population in South India in the context of chronic disease epidemiology (CURES-68). Public Health Nutr. 2011;1494:591–8.View ArticleGoogle Scholar
- Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, et al. The seventh report of the joint national committee on prevention, detection, evaluation and treatment of high blood pressure (JNC-7). JAMA. 2003;289:2560–71.View ArticlePubMedGoogle Scholar
- World Health Organization. The Asia Pacific perspective. Redefining obesity and its treatment. World Health Organization. International Association for the study of Obesity and International Obesity Task Force. Melbourne: International Diabetes Institute; 2000.Google Scholar
- Microsoft Corporation. Microsoft Excel version 10.0. Redmond, USA; 1998-2009.
- Census of India 2011 database. Government of India, Ministry of Home Affairs, Office of the Registrar General and Census Commissioner. 2011. http://www.censusindia.gov.in/2011census/C-series/C-13.html. Accessed 15 Jan 2015.
- StataCorp: College Station TX USA 1985-2013.
- Dohoo I, Martin W, Stryhn H. Methods in Epidemiologic Research. Charlottetown: VER Inc; 2012.Google Scholar
- Burke V, Beilin LJ, Simmer K, Oddy WH, Blake KV, Doherty D, Kendall GE, et al. Predictors of body mass index and associations with cardiovascular risk factors in Australian children: a prospective cohort study. Int J Obes. 2005;29(1):15–23.
- Pramod Sing GC, Nair M, Grubesic RB, Connell FA. Factors associated with underweight and stunting in rural Terai of eastern Nepal. Asia-Pacific J of Public Health. 2009;21(2):144–52.View ArticleGoogle Scholar
- Martinez-Gonzalez MA, Martinez JA, Hu FB, Gibney MJ, Kearney J. Physical inactivity, sedentary lifestyle and obesity in the European Union. Int J of Obesity. 1999;23:1192–201.View ArticleGoogle Scholar
- Agrawal S, Ebrahim S. Prevalence and risk factors for self-reported diabetes among adult men and women in India: findings from a national cross-sectional survey. Public Health Nutr. 2011;15(6):1065–77.PubMed CentralView ArticlePubMedGoogle Scholar
- Misra R, Misra A, Kamalamma N, Vikram NK, Gupta S, Sharma S, et al. Difference in prevalence of diabetes, obesity, metabolic syndrome and associated cardiovascular risk factors in a rural area of Tamil Nadu and an urban area of Delhi. Int J Diabetes Dev Ctries. 2011;31(2):82–90.View ArticleGoogle Scholar
- Vijayakumar G, Arun R, Kutty VR. High prevalence of type 2 diabetes mellitus and other metabolic disorders in rural central Kerala. J Assoc Physicians India. 2009;57:563–7.PubMedGoogle Scholar
- Florencio TM, Ferreira HS, de Franca AP, Cavalcante JC, Sawaya AL. Obesity and undernutrition in a very-low-income population in the city of Maceio, northeastern Brazil. Br J Nutr. 2001;86:277–84.View ArticlePubMedGoogle Scholar
- Steyn K, Bourne L, Jooste P, Fourie JM, Rossouw K, Lombard C. Anthropometric profile of a black population of the Cape Peninsula in South Africa. East Afr Med J. 1998;75:35–40.PubMedGoogle Scholar
- Doak C, Monteiro C, Adair LS, Bentley ME, Popkin BM. Coexisting overweight and underweight within households is related to the nutrition transition. FASEB. 2000;14:A792.Google Scholar
- Du S, Lu B, Zhai F, Popkin BM. The nutrition transition in China: a new stage of the Chinese diet. In: Caballero B, Popkin BM, editors. The nutrition transition: Diet and disease in the developing world. London: Academic; 2002. p. 205–22.View ArticleGoogle Scholar
- Kaur P, Rao SR, Radhakrishnan E, Ramachandran R, Venkatachalam R, Gupte MD. High prevalence of tobacco use, alcohol use and overweight in a rural population in Tamil Nadu. India J Postgrad Med. 2011;57(1):9–15.View ArticlePubMedGoogle Scholar
- Hamilton C, Filardo G. The dangers of categorizing body mass index. Eur Heart J. 2006;27(23):2903–4.View ArticlePubMedGoogle Scholar
- Royston P, Altman DG, Sauerbrei W. Dichotomozing continuous predictors in multiple regression: a bad idea. Stat Med. 2006;15:127–41.View ArticleGoogle Scholar
- Taylor JMG, Yu M. Bias and efficiency loss due to categorizing an explanatory variable. J Mult Anal. 2002;83:248–63.View ArticleGoogle Scholar
- Mamidi RS, Kulkarne B, Singh A. Secular trends in height in different states of India in relation to socioeconomic characteristics and dietary intakes. Food Nutr Bull. 2011;32(1):23–234.View ArticlePubMedGoogle Scholar
- Shetty P. Nutrition transition in India. Public Health Nutr. 2002;5(1A):175–82.View ArticlePubMedGoogle Scholar
- Preiss D, Thomas LE, Wojdyla DM, Haffner SM, Gill JMR, Yates T, et al. Prospective relationships between body weight and physical activity: an observational analysis from the NAVIGATOR study. BMJ Open. 2015;5(8):e007901.PubMed CentralView ArticlePubMedGoogle Scholar
- Hausenblas HA, Symons Downs D. How much is too much? The development and validation of the exercise dependence scale. Psychol Health. 2010;17(4):387–404.View ArticleGoogle Scholar
- Iqbal BA. India's hungry and enslaved: Badar Alam Iqbal examines two major problems confronting India. NZ Int Rev. 2014;39(4):24–6.Google Scholar
- Ramachandran A, Snehalatha C, Baskar ADS, Mary S, Sathis Kumar CK, Selvam S, et al. Temporal changes in prevalence of diabetes and impaired glucose tolerance associated with lifestyle transition occurring in the rural population in India. Diabetiologia. 2004;47:860–5.View ArticleGoogle Scholar
- Little M, Humphries S, Patel K, Dewey C. Decoding the type 2 diabetes epidemic in rural India: patients' knowledge, explanatory models, and narratives. Med Anthro. 2016: In press.
- Government of India. Census of India 2011: Provisional Population Totals Urban Agglomerations and Cities. 2011. http://www.censusindia.gov.in/2011-prov-results/paper2-vol2/data_files/India2/1.%20Data%20Highlight.pdf. Accessed 10 Feb 2015.
- Samuel P, Antonisamy B, Raghupathy P, Richard P, Fall CHD. Socio-economic status and cardiovascular risk factors in rural and urban areas of Vellore, Tamilnadu, South India. Int J Epidemiol. 2012;41:1315–27.PubMed CentralView ArticlePubMedGoogle Scholar
- Pandey R, Gupta R, Misra A, Sing V, Agrawal A, Dey S, et al. Determinants of urban-rural differences in cardiovascular risk factors in middle-aged women in India: A cross-sectional study. Int J Cardiol. 2013;163:157–62.View ArticlePubMedGoogle Scholar
- Joshi SR, Anjana RM, Deepa M, Pradeepa R, Bhansali A, Dhandania VK, et al. Prevalence of dyslipidemia in urban and rural India: The INDIAB Study. PLoS One. 2014;9(5):e96808.PubMed CentralView ArticlePubMedGoogle Scholar
- Deepa M, Pradeepa R, Rema M, Mohan A, Deepa R, Shanthirani S, Mohan V. The Chennai Urban Rural Epidemiology Study (CURES) – Study design and methodology. J App Phys India. 2003;51:863–870.Google Scholar
- Office of Technology Assessment. Health care in rural America (OTA-H-434). 1990. https://www.princeton.edu/~ota/disk2/1990/9022/9022.PDF. Accessed 9 Nov 2015.
- Rousseau N. What is rurality? Occas Pap R Coll Gen Pract. 1995;71:1–4.PubMedGoogle Scholar
- Government of India. Census of India 2011: Provisional Population Totals Urban Agglomerations and Cities. http://www.census2011.co.in/data/village/644087-anchetty-tamil-nadu.html. Accessed 9 Nov 2015..
- Dewey R. The rural-urban continuum: real but relatively unimportant. Am J Sociol. 1960;66(1):60–66.View ArticleGoogle Scholar
- Rourke J. In search of a definition of 'rural'. Can J Rural Med. 1997;2(3):113–5.Google Scholar
- Leduc E. Defining rurality: a general practice rurality index for Canada. Can J Rural Med. 1997;2:125–31.Google Scholar
- Faulkner HW, French S. Geographic Remoteness: Conceptual and measurement problems. Canberra: Bureau of Transport Economics; 1983. p. 54.Google Scholar
- Cloke PJ, Edwards G. Rurality in England and Wales: a replication of the 1971 index. Reg Stud. 1986;20:289–306.View ArticleGoogle Scholar
- Patel V, Kleinman A. Poverty and common mental disorders in developing countries. Bull World Health Organ. 2003;81:609e–615.Google Scholar
- Mendenhall E, Shivashankar R, Tandon N, Ail MK, Venkat Narayan KM, Prabhakaran D. Stress and diabetes in socioeconomic context: A qualitative study of urban Indians. Soc Sci Med. 2012;75:2522–9.PubMed CentralView ArticlePubMedGoogle Scholar
- Martinez JA. Body-weight regulation: causes of obesity. Proc Nutr Soc. 2000;59(3):337–45.View ArticlePubMedGoogle Scholar
- Kinra S, Bowen LJ, Lyngdoh T, Prabhakaran D, Reddy KS, Ramakrishnan L, et al. Sociodemographic patterning of non-communicable disease risk factors in rural India: a cross-sectional study. BMJ. 2010;341:c4974.PubMed CentralView ArticlePubMedGoogle Scholar
- Gaiha R, Jha R, Kulkarni VS. Obesity, affluence, and urbanization in India. ASARC working paper. 2010. https://ccep.crawford.anu.edu.au/acde/asarc/pdf/papers/2010/WP2010_10.pdf. Accessed 8 November 2014.
- Sobal J, Stunkard AJ. Socioeconomic status and obesity: A review of the literature. Psychol Bull. 1989;105(2):260–75.View ArticlePubMedGoogle Scholar
- Davidar P, Arjunan M, Puyravaud JP. Why do local households harvest forest products? A case study from the southern Western Ghats, India. Biol Cons. 2008;141:1876–84.View ArticleGoogle Scholar
- Silori CS, Mishra BK. Assessment of livestock grazing pressure in and around the elephant corridors in Mudumalai Wildlife Sanctuary, South India. Biol Cons. 2001;10:2181–95.View ArticleGoogle Scholar
- National Readership Studies Council. NRS 2006: Key Findings. 2006. http://www.slideshare.net/targetseo/national-readership-studies-india-nrs-2006. Accessed June 17 2015.
- Radhakrishnan RR. Free TV sets distribution begins. The Hindu. http://www.thehindu.com/todays-paper/tp-national/tp-tamilnadu/free-tv-sets-distribution-begins/article3076310.ece. Accessed 16 June 2015.
- Electronics Corporation of Tamil Nadu. Free television scheme for people of Tamil Nadu. http://www.elcot.in/free_colortv.php. Accessed 16 June 2015.
- Kuriyan R, Bhat S, Thomas T, Vaz M, Kurpad A. Television viewing and sleep are associated with overweight among urban and semi-urban South Indian Children. Nutr J. 2007;6:25.PubMed CentralView ArticlePubMedGoogle Scholar
- Brown W, Williams L, Ford JH, Ball K, Dobson AJ. Identifying the energy gap: Magnitudes and determinants of 5-year weight gain among midage women. Obes Res. 2004;13(8):1431–41.View ArticleGoogle Scholar
- Adinatesh KV, Prashanr RK. Prevalence of childhood obesity among school children in Karimnagar town. MRIMS J Health Sci. 2013;1(1):8–11.Google Scholar
- Bamshal M, Kivisild T, Scott Watkins W, Dixon ME, Ricker CE, Rao BB, et al. Genetic evidence on the origins of Indian caste populations. Genome Res. 2001;11:994–1004.View ArticleGoogle Scholar
- Locke AE, Kahali B, Verndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206.PubMed CentralView ArticlePubMedGoogle Scholar
- Ng M, Freeman M, Fleming T, Robinson M, Dwyer-Lindgren L, Thomson B, et al. Smoking prevalence and cigarette consumption in 187 countries, 1980-2012. JAMA. 2014;311(2):183–92.View ArticlePubMedGoogle Scholar
- Rani M, Bonu S, Hja P, Nguyen SN, Jamjoum L. Tobacco use in India: prevalence and predictors of smoking and chewing in a national cross sectional household survey. Tob Control. 2003;12:e4.PubMed CentralView ArticlePubMedGoogle Scholar
- Plurphanswat N, Rodu B. The association of smoking and demographic characteristics on body mass index and obesity among adults in the U.S., 1999-2012. BMC Obes. 2014;1:18.PubMed CentralView ArticlePubMedGoogle Scholar
- Jacobs DR, Gottenborg S. Smoking and weight: the Minnesota Lipid Research Clinic. Am J Public Health. 1981;71:391–6.PubMed CentralView ArticlePubMedGoogle Scholar
- Albanes M, Jones Y, Micozzi MS, Mattson ME. Associations between smoking and body weight in the US population: Analysis of NHANES II. Am J Public Health. 1987;77:439–44.PubMed CentralView ArticlePubMedGoogle Scholar
- Pednekar MS, Gupta PC, Shukla H, Hevert JR. Associations between tobacco use and body mass index in an urban Indian population: implications for public health in India. BMP Public Health. 2006;99:1–5.Google Scholar
- Kumar P, Mruthyunjaya, Dey MM. Long-term changes in Indian food basket and nutrition. Econ Polit Weekly. 2007;42(35):3567–3572.
- Ailhaud G, Massiera F, Weill P, Legrand P, Allesandri JM, Guesnet P. Temporal changes in dietary fats: Role of n-6 polyunsaturated fatty acids in excessive adipose tissue development and relationship to obesity. Prog Lipid Res. 2006;45:203–36.View ArticlePubMedGoogle Scholar
- Willett WC. Dietary fat plays a major role in obesity: no. Obes Rev. 2002;3:59–68.View ArticlePubMedGoogle Scholar
- Bray GA, Popkin BM. Dietary fat does affect obesity! Am J Clin Nutr. 1998;68:1157–73.PubMedGoogle Scholar
- Nelson LH, Tucker LA. Diet composition related to body fat in a multivariate study of 203 men. J Acad Nutr Diet. 1996;96(8):771–7.Google Scholar
- Tucker LA, Kano MJ. Dietary fat and body fat: a multivariate study of 205 adult females. Am J Clin Nutr. 1992;56(4):616–22.PubMedGoogle Scholar
- Simopolous AP. Essential fatty acids in health and chronic disease. Am J Clin Nutr. 1999;70 Suppl 1:560–9.Google Scholar
- Caballero B. A nutrition paradox – underweight and obesity in developing countries. N Engl J Med. 2005;352(15):1515–6.View ArticleGoogle Scholar
- Buettner R, Scholmerich J. High-fat diets: Modeling the metabolic disorders of human obesity in rodents. Obesity. 2007;15(4):798–808.View ArticlePubMedGoogle Scholar
- Romieu I, Willett WC, Stampfer MJ, Colditz GA, Sampson L, Rosner B, et al. Energy intake and other determinants of relative weight. Am J Clin Nutr. 1988;47(3):406–12.PubMedGoogle Scholar
- Maron DJ, Fair JM, Haskell WL. Saturated fat intake and insulin resistance in men with coronary artery disease. Circulation. 1991;84:2070–4.View ArticleGoogle Scholar
- Dreon DM, Frey-Hewitt B, Ellsworth N, Williams PT, Terry RB, Wood PD. Dietary fat: Carbohydrate ratio and obesity in middle-aged men. Am J Clin Nutr. 1988;47:995–1000.PubMedGoogle Scholar
- Gillis LJ, Kennedy LC, Gillis AM, Bar-Or O. Relationship between juvenile obesity, dietary energy and fat intake and physical activity. Int J Obes. 2002;26(4):458–63.View ArticleGoogle Scholar
- National Institute of Nutrition. Dietary Guidelines for Indians: A Manual. Indian Council of Medical Research. 2010. http://ninindia.org/DietaryguidelinesforIndians-Finaldraft.pdf. Accessed 15 February 2015.
- Gallengher D, Mariolein V, Sepu Alveda D, Pierson RN, Harris T, Heymsfield SB. How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol. 1996;143:228–39.View ArticleGoogle Scholar
- Bingham S, Luben R, Welch A, Ling Low Y, Tee Khaw K, Wareham N, Day N. Associations between dietary methods and biomarkers, and between fruits and vegetables and risk of ischaemic heart disease, in the EPIC Norfolk Cohort Study. Int J Epidemiology. 2008;37(5):978–87.


text:
Summer Teen Program in Barcelona, Spain
Program Duration
1-2 Weeks, 2-4 Weeks
April, July-September, October
Type of Programs
- Experiential Learning
- Language Immersion
- Student Exchange
- Spanish
Languages Used as a Medium of Teaching
- Spanish
Cost in US$:
From USD 756 for 1 week
Program Fees Include:
* Language lessons Standard (20 lessons/week) or Intensive (30 lessons/week)
* Max of 15 students per class
* Use of teaching materials & classes are 45 minutes long
* Shared accommodation in either Host family or school residence
* Meal plan - 3 meals daily
* Travel Medical Insurance for the program duration
* Weekly activity program
* One full day & one half day excursion per week
* T-shirt
* Language certificate at end of program
* Airport transfer from/to Barcelona airport on arrival and departure days.
* Pre-departure information
* 24 hour Emergency hotline on travel days
Credit Available
Experience Required
This Program is open to
Worldwide Participants.
Participants Travel
Typically Participants Work
in Groups
Application Process Involves
- Written Application
Typically The Application Process Time is
48 hours

subset: commoncrawl


text:
Hi everyone. I just would like to make this clear. I am not a fan of anime very much. Well I watch One Piece from time to time. But not others very much... I am not planning to be on this forum for very long. I just have one lousy question and then I will go away. So please at least someone answer my question if I am going to get banned.
Does anyone know which episode is the one where Dorothy is fighting a squid-like monster? It grabs her and starts squeezing the life out of her. This is not explicit. Although the stupid suggested video links are, which is prolly why I got banned at Animesuke. >_> I just want to know what she is fighting or what episode it is so I can look it up myself. I do not want waste time and money to watch the series to find out. Only reason I ask here is because there isn't a forum for MAR and Animesuki banned me forever. No warning or nothing, just banned off the spot and the thread was never approved. -_-; I would post the link for the video but the stupid suggested videos lead to more explicit stuff... Private message me if you think you can help me. Thanks.


text:
Today, I re-sat an expensive exam I'd flunked 6 months previously. The same impossible multiple choice questions appeared, which I'd been unable to find the answers to, even with all my textbooks and the internet. FML
By God,NotAgain,Please - / Wednesday 12 April 2017 18:11 / Pakistan - Karachi
Add a comment - Reply to : #
You must be logged in to be able to post comments!Create my account Sign in
Top comments
leagacysgirl | 39
Probably needs that exam to complete a degree.
Cassieloub | 41
Yes because looking in textbooks is clearly cheating....
RichardPencil | 41
Trying to memorize answers from previous tests is.
RichardPencil | 41
It can be. The reality is a lot of students make databases of previous questions to cheat.
Brightbulb | 41
that's not how this FML seems to come out to me though. More that they sat the exam, did poorly because they didn't know answers to complex questions and tried to find what the answers would be so they would be more prepared next time. when my human's sister sat the bar exam her first time that is what she did to better prepare, after sitting it the second time she passed.
By ZoroMiHawk | 47
Because not all questions have a specific answer. Some questions are thought-provoking and require you to apply your reasoning according to whatever the question and choices are in order to arrive at the most logically correct answer/s
Lalala579121 | 47
Don't be silly, everyone knows that multiple choice questions are only testing how well you can play "eenie meenie minie mo".
Loading data…


text:
SAVE UP TO 60% ON CLEARANCE ITEMS. Take an additional 20% off sale with the RH Members Program. ▸
Porcelain Tea Set - Crown
$59 Member
Finely crafted of porcelain and embossed with a quintessential crown, our tea set turns a timeless playtime ritual into a royal repast for four.
More Details & Dimensions +
Porcelain Tea Set - Crown
$59 Member


text:
Explore Engine Trainer, Jet Trainer and more!
Explore related topics
Hongdu K-8 Karakorum Light Attack and Jet Trainer Aircraft.
Zimbabwe Air Force Chinese built Hongdu K-8 (or JL-8) Karakorum, single engine trainer & light attack aircraft.
Hongdu K-8 Karakorum
Hongdu K-8 Karakorum
21 November 1990 first flight #flighttest of the Hongdu JL-8 / K-8 Karakorum, Chinese jet trainer
Royal Air Force T1 Hawk Trainer Jet Aircraft by Defence Images
Bangladesh has taken delivery of its first eight Hongdu K-8 "Karakorum" trainers that will gradually replace existing Cessna T-37s.In total Bangladesh ordered nine Hongdu K-8 "Karakorum" which will all operate from air base Dhaka.Will help train pilots destined for frontline aircraft such as: Chengdu J-7 and RAC MiG-29
A Tactical Electronic Warfare Squadron Thirty-Three (VAQ-33) Douglas ERA-3B "Skywarrior" Attack Aircraft, alongside a Fleet Composite Squadron One (VC-1) Douglas TA-4J "Skyhawk" trainer attack aircraft north of Oahu, Hawaii.
fighter jet
F-22 Raptor 8


text:
August 13, 2014
According to FleetCor, the acquisition of the Tennessee-based Comdata is expected to provide a number of benefits such as expanding FleetCor's North American fuel card business into new, unserved markets; enabling entry into the virtual payments business; and dramatically increasing FleetCor's overall scale and earnings base.
"We have followed Comdata's growth and development for many years, and are excited today to be bringing the companies together" said Ron Clarke, chairman and chief executive officer of the Georgia –based FleetCor Technologies. "Comdata's virtual payments business will add a completely new growth leg to FleetCor. We believe that the combination will result in significant synergies as we implement our operating disciplines to their portfolio of businesses."
The deal is expected to close by the end of the year.
We offer e-newsletters that deliver targeted news and information for the entire fleet industry.
Scott Sutarik from Geotab will answer your questions and challenges
Steve Carlson from Xantrex will answer your questions and challenges
On the Spot: Volvo Trucks VNR Regional Tractor [Video]
On The Spot: International RH Regional-Haul Tractor [Video]
MATS Attendees Talk Trucking Issues [Video]
Kevin Knight Talks About the Swift Merger [Video]
High-Torque, High-Power Starters Available from Prestolite Electric
Ancra's Logist-A-Panel Helps Secure Cargo
Air-Weigh Updates LoadMaxx Trailer Scale
TomTom Announces Comprehensive ELD Solution


text:
One thing you can set your watch to. No matter the season, the record, or time of year, a loss in Montreal will drive you crazy. Losing in Montreal makes you think thoughts that aren't rational. It drives you to your kitchen where you just start eating things because you're so jammed up. You take your dog for a real long walk after losses in Montreal. You probably lost some inhibitions. If a girl rolls over in bed and wants to have sex the night of a loss in Montreal, you kick her onto the floor, take all the pillows downstairs, and sleep on the couch. Maybe even divorce her. The hate for Montreal built all game, and then Kris Letang carried the anger of an entire fanbase toward PK Subban at the end of overtime. At that exact moment, you wished Letang threw Subban through the glass and then punched him in the face until Subban turned to slush. Nothing like a game in Montreal. Overall, the Pens were still able get a point, which is fine. After a winning streak, losses feel like valleys. Have to find the emotional even keel again. Pens get a chance to rest up until Saturday, 2:00pm, against Winnipeg. Cap after readmore:
Typical fast pace opening, and Montreal getting away with infinite penalties. Both teams skated their sacs off. Sullivan had that first solid shift. P.K. Subban inserted himself into the game as he trolled Malkin while Malkin laid on the ice after trying to hit Subban. What a dick. Subban basically intentionally fell down on his own to avoid the hit. Malkin's gained notice recently for "ducking" under checks, but that's along the boards coming down the wing. What Subban did was something you see in a Care Bears episode. Marc-Andre Fleury was into the game big-time. Montreal had a ton of breakaways. MAF was coming up roses all game. Of course Paul Martin had to be on the ice for one of those breakaways. It was a joint fault between Dupuis and Martin not covering the trailer. MAF bailed him out, so who cares. We personally don't dislike Paul Martin. Paul Martin Powerball isn't to job him. If the timing was right, we would've done a Rob Scuderi Powerball back in the day. It's about a defenseman trying to get on the scoreboard. Damn it. Joe Vitale got tripped somewhere. But maybe he fell on his own.
The Pens got the first two powerplays of the game. It was in the back of your mind that these early PP failures would come back to haunt the Pens somehow.
The first powerplay was a mess, and the first unit sat early. On the second powerplay about 5 minutes later, the Pens had a few chances but could not knock one past Price. Montreal was still getting unlimited chances.
They finally cashed in when Matt LeBlanc from "Friends" beat MAF on a breakaway. 1-0.
The two teams traded powerplays later on. James Neal got pissed at Tomas Plekanek's mock turtleneck and took a penalty. Pens killed it.
Scott Gomez took some bizarre penalty, but the Pens couldn't do anything with the chance. It was a frantic ending to the period. Craig Adams got called for a penalty. Pens would be shorthanded going into the third. Frustration city.
MAF had one of the prettiest saves of the year on Erik Cole late in the second. Can't find video of it. Here's the pic:
Erik Cole sucks.
Going into the third, the Pens needed a kill. But they did one better. They got a shorthanded goal.
Fifth Ave Joe Vitale drove down the right side and threw a puck to the middle. The puck hit Dupes' skates. No kicking motion. 1-1.
Things evened out from there. Both goalies making big-time saves.
And then the Habs got a bounce. Lars Ulrich came down the wing, and even he didn't know what he was gonna do with the puck. MAF thought it was gonna be a dump and was jumping out to get it. Ulrich then flung it to the net; it hit the post then off of MAF's ass.Unrealistic bounce. 2-1.
People saying that goal is vintage Fleury haven't really watched his career. Sure, he has issues handling the puck, but nearly NHL goalie of all time has had to deal with a bizarre play like the one last night. MAF doesn't routinely give up long shots. It's a fluke. It's the only way the Habs can stay in games anymore.
Saving MAF was the fact that the Pens got it right back. Kunitz-Malkin-Neal got things going. Malkin gets knocked down by Gill. Again, last season, Malkin goes and crosschecks Gill. Instead, Malkin gets up and goes behind the net. Kunitz pushes it to him. Malkin throws the puck in front, finds Neal.
Snipe. 2-2.
Then it was back and forth again. As usual, the Pens had to overcome being called for a late penalty. Great effort by Matt Cooke on the penalty kill.
By the time the penalty was over, it was try to get the point city.
To OT we go.
OT was a battle. Subban and Malkin went toe-to-toe on every shift they were out there. Letang had a huge chance, as did some idiot from Montreal.
Toward the end of OT was when an exclamation point was put on overtime.
First came this incident:
Not exactly sure that Malkin is talking to Letang here. Looks like he's chirping the Habs bench and then turns his head for some reason in mid-sentence.
Then came the reason we're buying Letang jerseys:
The minute you see that white jersey go flying into the picture the blood pressure in your body reaches critical mass.
Gif of the play via Highheelsandhockey
Balls in your mouth every time someone picked up the puck at center ice. MAF and Price were taking care of business.
Malkin did this:The Pens had unlimited chances to win it. Then Thomas Plekanec and his mock turtle neck step over the boards:
It was up to Jason Williams for the Pens. He mad a solid move, looked like he beat Price for a split second. But no.. GAME.
- At least Montreal won't make the playoffs.
- Scott Gomez sucks.
- Vintage Montreal crowd last night.
- Good replays by Root Sports last night actually. Broke down a few things they usually miss.
- Ultimately when a Pens fan appears on "Intervention," he or she will talk about the trigger of drug addiction as a loss in Montreal.
NHL News
Delivered to your inbox
You'll also receive Yardbarker's daily Top 10, featuring the best sports stories from around the web. Customize your newsletter to get articles on your favorite sports and teams. And the best part? It's free!
|Best of Yardbarker
|NFL News
|MLB News
|NBA News
|NHL News
Today's Best Stuff
For Publishers
Company Info
Follow Yardbarker
|© 2017 Yardbarker, Inc. All rights reserved. Use of this website (including any and all parts and components) constitutes your acceptance of these Terms of Use and Privacy Policy. (What's New?)


text:
As we were leaving Sebastian's nutrition clinic the doctor said 'You are doing a good job. But you can do better.' Um, thanks?
I bit my lip to hold back the tears. I felt defeated. Again. It had been a tough week and rough couple of days especially. Of course he had no idea what we are going though right now. Not many people do.
Never mind that he arrived at the appointment at the end, after I'd argued with the nutritionist about Cisepride, a drug that was taken off the market in several countries because it can kill you. And to use it in Canada you have to get an EEG and special permission. Probably sign a waiver too. Never mind he was just poking his head in and came to make sure that he could tick all the boxes; 'Has Sebastian been to the eye doctor, has he been to the dentist? Is so and so still his neurologist?' Then looking at his growth chart. Never mind that Sebastian grew 5cm and is bursting out of his wheelchair again. His weight increased but not enough. It is never enough. And apparently it is my fault.
Four weeks ago Sebastian was in the ER getting diagnosed with strep throat. He couldn't eat for a week. I have him a fully organic blended diet, made by me, via gtube. Meals approved and calories checked by another nutritionist. I increased his feeds since his throat hurt too badly to eat. The antibiotics were extremely painful for his stomach and he couldn't sleep for almost the full week either until we switched them so even the food by tube was intolerable at times.
I feel incredibly proud and happy that for four weeks Sebastian has had a fully blended diet. He's also eating orally again. And he's living without constipation naturally. He goes through bottles of flax oil, avocado oil, and coconut oil quicker than I replace one bottle of olive oil for the whole family. His blends include coconut milk from a can and avocado. Plus countless veggies and various proteins. There are carbs in there too like quinoa, cous cous, and rice. I was actually looking forward to his weight check because I thought it would be higher even despite his illness. And it was. But not enough.
I can do better.
I'm going to wave my magic wand and his reflux will be gone. That's it. Because I can do better. I'm going to stuff him like a turkey until he's bursting. Because I can do better. I'm going to hook him up to a tube to feed him all night. Forget sleep. Never mind that his reflux and gassy tummy will keep him up all night. Seizures smeezures. Because I can do better.
They have been following Sebastian since we moved here. 6.5 years plus. First they offered Duocal. We used it despite the reflux it caused. Then they pushed for a g-tube and when we finally got it they pushed Neocate formula even though I asked for help with a blended diet. They offered Pediasure and other enhanced formulas that Sebastian can't tolerate because he can't have dairy and soy. They offered Miralax for constipation. They increase his Prevacid and talked about adding another medicine to help with motility. One makes him lethargic and the other one could kill him. They just push this and that and tell me more more more. Add more oil. Push him til he pukes. Because you can do better.
Why can't THEY do better? Why can't they find out what is best for my son without just pumping him full of corn syrup solids and medications? Why can't they fix his reflux? Why didn't they fix his constipation?
Guess who did?
For four weeks Sebastian has had a fully blended diet by tube. He has had no reflux from food intake and no constipation.
I do my best. Every day. And every night. When I'm blending food and separating it into glass jars to freeze. When I'm waking up early to get his pump ready and prepare his food for the day.
When I'm lying awake thinking about the list of things I will do tomorrow to make my son's life better. Ticking all the boxes in my own mind.
I wake up and do it all again. Every day.


text:
Can the coupon be used on the Saturday buffet?
Janis M. asked on Oct 7, 2012
Hi Janis! I'm sorry, but it looks like the business was not able to get to your question. They would be more than happy to help you directly. The business can be reached at (281) 440-5310.
Kristi D. from Groupon replied on Oct 10, 2012


text:
And if British guards do not meet US standards, the Department of Homeland Security says American marshals should take their place.
The controversial move is part of a package of measures proposed by the White House in a plan to make it easier for foreigners to enter the US by expanding the visa waiver programme.
It is certain to cause a new storm over guns on planes, which the British Airline Pilots Association strongly opposes.
Three years ago British pilots threatened to strike over armed sky marshals.
The row died down and there has never been an official admission by British authorities that armed guards fly on British planes.
But the government will have to face up to the issue again under the new US plan. It proposes that undercover guards should dress like tourists, but carry Sig Sauer 9mm pistols with special bullets designed to stop people while minimising damage to the plane itself.
The American plan, which goes before Congress in the New Year and is backed by the White House, says: 'The United States benefits from the enhanced security of allowing US Federal Air Marshals to operate on board international flights to and from the United States.
'We will ask visa waiver programme countries to co-operate in the air marshal programme.'
A senior American security official said: 'We will either want to put our own people on foreign carriers' planes or be satisfied that their people are trained to our standards.'
But security experts point out that America has trouble recruiting enough air marshals for all its own airlines' international and domestic flights, let alone supplying them for foreign carriers. The visa waiver programme allows people from 27 mostly European countries, including Britain, to travel to the United States and stay up to 90 days without a visa. Stung by a drop in tourism and criticism that the US has become inhospitable since 9/11, President Bush wants to expand the visa waiver programme to other 'friendly' countries from Eastern Europe and Asia that have helped in Iraq and with the war on terror.
His proposal includes plans to copy an Australian system requiring travel agents to supply authorities with personal details about travellers which could be checked against terrorism and criminal databases.
The Bush plan also wants tighter controls over lost or stolen foreign passports, a fast-track process for foreigners ordered by US courts to be deported, and improved security at many international airports.
The British government and airlines have always refused to comment about armed guards.
A Department for Transport spokesman said: 'No information or operational detail about air protection officers or deployment of officers is made public.'
British Airways said: 'We never discuss security.'
But industry sources say the UK started training undercover air guards in 2002.
Many are police firearms officers who take an eight-week course at the Metropolitan Police's Specialist Training Centre in Gravesend, Kent - one of the best in the world.
It is not known how many of them ever actually fly as sky marshals. One senior officer said: 'Not many of us want to spend our careers cooped up in planes.'
America is more open about its armed air marshals. They were introduced by President Kennedy in 1961 after a worldwide spate of hijackings.
But there were only about 30 of them until a massive recruitment drive was launched after the September, 2001 attacks.
There are now more than a thousand costing Homeland Security £500 million a year.
After strong lobbying by US pilots, they are now also allowed to carry guns.
- More about:
- Air Transport
- Gravesend (kent)
- Identity Cards
- Police
- Security
- Travel


text:
|171 m²
|Tipo de imóvel
|Estadia Mínima
|2 - 7 noites
1850+ pés 4 casa temporada quadrado com 3 quartos e um loft e 2 casas de banho completas. Há um quarto no primeiro andar com uma cama queen size e casa de banho completa outro lado do corredor. O segundo andar tem 2 quartos; um com uma cama de casal e outro com 2 camas de solteiro, há um loft aberto com um sofá que se abre para uma cama de tamanho completo e mais rodadas de banho completa fora do segundo andar. O primeiro andar tem um conceito aberto cozinha, refeitório e área com tetos de catedral e um fogão a lenha vivo. O convés é 40 x 10 e é embebida sol para a maior parte do dia. Fora da área de jantar é um alpendre para bug livre exercício do NH ar fresco.
A cave é terminado com um 56 'polegadas TV, um jogo de vídeo contendo 40 jogos dos anos 80 e uma placa de dardo. É um ótimo lugar para as crianças a sair e deixar os adultos sozinho.
A "praia comunidade privada 2500 é um curta de 10 minutos a pé até a colina de trenó, mas o plano em 15 minutos de caminhada de volta até a colina.
Para o verão férias há um opcional semanal passe de estacionamento da praia. A praia é particular e requer um passe para estacionar a taxa foi de US $ 75 / semana para o 2016 e é definido pela Associação Suissevale. Presumo que a taxa será semelhante para o verão de 2017. Sem o passe você não pode estacionar o seu carro na praia.
**** Os hóspedes são responsáveis por seus próprios lençóis e toalhas. *****
* Ou seja, bens de papel. papel higiênico, toalhas de papel e guardanapos etc. estão na casa, mas deve ser reabastecido como eles são usados. Pay It Forward por favor! **
Our group of five vacationed at this property in February. Enjoyed sledding down the hill located right next door. Buckey's Bar and Restaurant within a few miles down the street was super convenient for dinner every night. We planned a day trip snowmobiling with Sweet Ride Snowmobiles in Madison and another day trip ice fishing in an icehouse provided by Hole in the Wall located in Wolfeboro. You could see the lake from the super nice view on the top deck. Bottom floor TV and game room is a great separate space for kids. Todd, the homeowner was extremely accommodating and helpful.
everything great. Driveway a little tricky in the ice and snow
My family and I were in Moultonborough for a family wedding. Renting Todd and Maryann's home for the weekend was a great decision. Planning and speaking with Todd was very easy, ( we went in a little early, and had to stay a little late). The 8 of us slept comfortably and there was plenty of room for relaxation and entertainment. The TV/game room downstairs was were our grandchildren hung out while we were home and the fully equipped kitchen fit all our needs. The absolutely beautiful association beach is a short walk. The fall foliage was perfect and It seems they have a full day of activities for summer fun!!! Making memories at this home with my family was an awesome decision!
Thank you Todd and MaryAnn, we loved it!!
My family was in Moultonborough for an Autumn wedding. We found the house to be perfect for our stay. Living space was very spacious for us. We had 6people. Bedrooms were comfortable, kitchen was well stocked with all you would need. The deck outside is very nice too. We did not have children with us, but there is a finished game room that looked like a great place to hang out. The owners were very easy to work with and quick to respond to my questions. I would definitely recommend this house.
We were in town for a wedding nearby and were pleasantly surprised that the location of the house was an even closer proximity to the location of the wedding than we originally thought. The house was great, lots of room, and we loved the finished basement. It was a rainy weekend, so the enclosed porch was great to sit and relax without getting wet. We had a few pre-wedding events to attend to on the day of check in, and Todd was great about letting us check in a little early. Overall, it is a great house, the owners were very flexible and easy to work with, and we are looking forward to coming back to stay for a weekend this summer!
It was great to have a dog friendly place to stay, and the hosts were very flexible with allowing us to check in early and leave a little later. The house was great, and location ideal to the activities we wanted to do. The only slight inconvenience was bringing our own towels and linens, which just meant more packing for us, but certainly not a deal breaker.
|Validade da Tarifa
|por noite
|Noite (fim de semana)
|por semana
|por mês
|Pacote ou Evento
Winter / Spring 2017
jan 2 2017 - jun 23 2017
Estadia mínima: 2 noite(s)
Summer 2017
jun 24 2017 - set 4 2017
Estadia mínima: 7 noite(s)
Fall / Winter 2017
set 5 2017 - dez 22 2017
Estadia mínima: 2 noite(s)
Tarifa Básica
Estadia mínima: 2 noite(s)
|Cleaning and Trash
For summer rentals there is an optional weekly beach parking pass. The beach is private and requires a pass to park the fee was $75 / week for the 2016 summer and is set by the Suissevale Association. I assume the fee will be similar for summer 2017. Without the pass you can't park your car at the beach.


text:
Items in this set Merchant links are sponsored
Similar styles
Stud Muffin
Studs add a cool effect to any drab piece of clothing! For this contest, show me your sets featuring an outfit with studs on any article of clothing. See featured items for suggestions.
3 Days ~ 3 Sets ~ 12 Winners
3 Days ~ 3 Sets ~ 12 Winners


text:
A King Is Born (Choral Score)
By Gorieb/Hosman
To play the media you will need to update yourFlash plugin.
TTBB choir, tenor or soprano voice solo, piano, optional Marimba, percussion (Shaker, conga drums), bass guitar - Medium
Composed by Gorieb/Hosman. Arranged by Tim Sarsany. Luther College Series. Christmas. Choral score. Published by MorningStar Music Publishers (MN.50-1135).
Item Number: MN.50-1135
This infectious calypso is something different for our catalog, but was so delightful we couldn't stop smiling. For TTB/TBB/TTBB, prominent tenor or soprano soloist, piano, and optional percussion and bass guitar, this piece will be a total hit with your singers, congregation, or concert audience. There is an SATB (divisi) version (50-1125). Full score and instrumental parts available seapartely:
Full ScoreInstrumental Parts.


text:
The steampunk genre brings back a bit of the Victorian era and the charm that we associate with that time period. If you're a fan of steampunk and want to do something fun with it, consider inviting some friends and family over for your own steampunk Victorian tea party. Here is … [Read more...]
Printable Steampunk Halloween Party Invitations and Decor
Halloween is a time for costumed parties and gatherings, which provides the perfect opportunity for dressing up in fun Steampunk fashions. You can also add a touch of Steampunk flair to any Halloween party by inviting your guests using these great printable Steampunk Halloween … [Read more...]
Cool Steampunk Books for Holiday Gifts
Whether you know someone who loves to read the Steampunk genre of literature, or who is eager to know more about the whole Steampunk cultural movement, including tips on fashion, Steampunk books make great gifts for the holidays. Steampunk science fiction novels make a great … [Read more...]
Steampunk Journals for Graduation
A high school or college graduation is an event that requires giving a special gift. A journal is always a great gift idea, as it gives the recipient a place to jot down their thoughts and feelings, and graduation is certainly a time when they'll be experiencing a lot of … [Read more...]
The Steampunk World's Fair 2017: Fires of Inspiration
Steampunk is a term used to describe a particular genre in which the past meets the future. Think of it as a type of fantasy and science fiction meets the Victorian era. To fit into the Steampunk genre, there must be some type of setting that takes place in the past, generally in … [Read more...]


text:
- Home
- News
- Sports
- Opinion
- Obituaries
- Features
- Special Sections
- Classifieds
- Public Notices
Forgot Password? | Not a User? Sign up
A Leitchfield man is behind bars on assault and stalking charges for what police say was a premeditated attack on a Hardin County man outside of his Rock Creek Road home in the Summit area.
If you currently subscribe or have subscribed in the past to the The News Enterprise, then simply find your account number on your mailing label and enter it below.
Click the question mark below to see where your account ID appears on your mailing label.
|ZIP Code:


text: The American College of Trial Lawyers, founded in 1950, recognizes the very best of the trial bars of the United States and Canada. The College's Fellows are chosen strictly by invitation and only after a rigorous, confidential investigation. Fellowship is limited to one percent of the lawyers in any individual State or Province. In practice, far less than one percent ever qualifies for this honor. In order to qualify for consideration, a lawyer must have practiced as a trial lawyer for at least 15 years.