Effect Of Urbanization On Obesity And Diabetes In India: Findings And Conclusion

Discuss about the Effect of Urbanization on Obesity and Diabetes in India.

India like any other nations in the world is experiencing high mortality rates due to the prevalence of obesity and diabetes (Bowen et al., 2011). A person is considered obese when his body mass index abbreviated as BMI is above 30kg/m2, with a range of 25 to 30kg/m2 defined as overweight. As it is evident,  diabetes is a chronic disease in which the body’s ability to respond or produce insulin (a hormone) is impaired, hence resulting in unusual metabolism of Carbs (CHOs) plus elevated levels of sugar in the body, especially urine and the blood. Over the decades the prevalence of diabetes has been witnessed in India especially in the urban centers. In the early 1980s only 5% of Indian adults living in urban centers had diabetes, but in 2004 the figure had already risen to 15% (Ebrahim et al., 2010). In the suburban areas of India, diabetes is less common, with a rate of 6% which is almost a third of the prevalence in urban areas.  Obesity has also been a contributing factor to the widespread of diabetes. Studies show that urban areas have registered high rates of obesity compared to rural areas, especially the case of India.

Ebrahim et al. in their study used a cross- sectional study to investigate the effect of urbanization on ‘obesity and diabetes in India.’ The hypothesis that rural-urban migrants had greater risks of being obese as well as developing diabetes was analyzed and compared with the rural dwellers

Using a framework of CVD hazard ratio, the study was conducted in four companies in the cities of Lucknow, Nagpur, Hyderabad, and Bangalore in the urban center of India. Out of these factories, the workers were recruited using an employer record as a sampling frame. In the recruitment, it was ensured that the workers were rural to urban migrants. Each of the recruited employees was asked to invite a non-migrant of their next kin of their age living in rural areas. When recruiting, the invitees were given priority in regard to gender and where the sex was the same the person close to the age of the worker was considered. Out of this ‘exercise,' the rural dwellers recruited were from 20 states compared to the 29 Indian states, hence showing a migratory trend of the four factories labor force. Out of the invited non-migrants, 25% of them were randomly selected to participate in the study. However, the study commenced on March 2005 and lasted for 31 months. In the study, standing height was taken using a stadiometer and the weight measured with shoes off and on light clothing. They were also examined for blood pressure and interviewed to obtain any data regarding alcohol consumption or tobacco use such as smoking of cigarettes.

Findings

Obesity and diabetes-related outcomes were also assessed where the diagnosis of diabetes was made using a WHO ‘fasting plasma sugar criteria’ of >7.0 millimoles per liter. ‘Homeostasis model assessment scores (HOMA)’ for estimating insulin impairment were also established using a ‘standard formula of blood glucose’ on the aspect of the initial approach. In the study, a dietary assessment was done using a food frequency questionnaire. To check for the worthiness of the questionnaire, sub-samples were pledged to fill questionnaires (n=185) and (n=305) following the ‘completion of the survey’ in an initial ‘data collection.’ Other 530 participants were subjected to the ‘reference’ technique of 24-hour dietary recalls that were used to authentify the food frequency questionnaire. Besides, their fat intake was measured.

Another method that was used in the study was ‘physical activity’ where the ‘interviewer-administered questionnaire’ was used to check the ‘physical activity’ plus other usual daily occurrences. The validation of the questionnaire was done in 45 urban as well as 49 rural participants via making comparisons with the 24-hour activity diary plus uniaxial accelerometer. Moreover ‘participants were’ urged to ‘attend fasting’ where the period of the final meal was noted. Their ‘blood samples’ with an exclusion of sugar assays was alienated and then stored. Lastly, men and women were analyzed each differently as it were foreseen that gender sensitivity might bring differences in the migration effects hence the cause of obesity and diabetes. Since the participants were from different factories and of different age, adjustments were made regarding such. As the rural group was expected to have fewer risks of obesity and diabetes compared to their counterparts in the urban group as postulates in the hypothesis, a trend test was done scoring the groups 1  up to 3 as well as using R .T or ‘likelihood ratio tests.’ Another hypothesis of whether the effect in migrant plus rural group was the same was also conducted and the analysis made using STATA 10.

Workers records showed that 21, 663 employees in the four cities/factories were available for the study. About 72% that is 15, 595 of those people were contacted of whom 88% about 13, 696 finished the assessment of eligibility for the study. Out of the 88%, 55% that is around 7, 595 were eligible for the inclusion reason being they were among the 25% who were randomly selected from the urban non-migrants. About 94% that is 7, 101 people conformed to complete the clinical examinations. Besides, factory employees living in suburban places and who traveled to their workplace every day were retracted from the analysis. There was no difference found in the mean age, migrant status, distance from the suburban area of origin as well as the marital status of the participants. Besides, self-reported widespread of CVD was 14.8% in non-responders a less percentage compared to the 21.0% of non-consenters and 19.4 % in responders. Ideally, there was also a difference in the smoking between responders as well as other groups.

Discussion and conclusion

Out of the 6510 participants who were in the analyses about 42% that is 2723 were females. In general, 2112 were rural to urban migrants, 2111 no-migrant suburban residents and 2287 were non-migrant city dwellers. Urban males were a bit older relative to the suburban men but of the same age bracket to those of migrant males. Most importantly, the majority of migrants had spent a considerable amount of time in the urban areas with men having a median of 26 years and females 21years.

Moreover, comparison of the risk factors as well as health issues between rural groups, migrants and urban has also been presented throughout the study. There was significant evidence in females and males of differences in body mass index between the suburban, urban and migrant people. Obesity was highly prevalent in urban women at a rate of 53% at 95% confidence interval and lowest in suburban men at 18% with the migrants in the intermediate position. Moreover, the occupation, age as well as the ‘factory adjustment odds’ of obesity lied in the range of 3 & 4 fold higher in migrant compared to the suburban women and men.

The urban plus migrant groups were equal concerning the ‘MET h/d of physical activity’ while the suburban group had a greater ‘MET h/.'  Alcohol consumption and smoking were limited among women, however, among the migrant men, they registered the least chances of smoking while the suburban men the most. Odds of hypersensitivity in urban as well as migrant men was almost double that of suburban men. Besides, increased odds were evident in women.

In both women as well as men, ‘fasting blood sugar levels’ were the same in migrant plus urban groups and lowest in the suburban groups. HOMA scores registered a hierarchical trend ‘from urban, migrant to suburban.’ The widespread of the disease was high in urban groups, followed by migrants and rural group the least. However, both migrant and urban males and females had over two-fold increased odds of obesity disease relative to the suburban participants.

A hypothesis that urbanization had “higher prevalence of obesity and diabetes” compared to the suburban non-migrants was highly supported by the findings (Ebrahim et al., 2010). On the other hand, the other hypothesis which migrants had ‘intermediate prevalence’ relative to city residents was not upheld. However rural-urban migration was connected with less physical activity and increased fat consumption in males & females relative to the rural residents, and this probably led to the greater ‘obesity levels & diabetes’ as it was witnessed in migrants. The gender difference witnessed in the study was not expected with the migration-linked differences in fasting blood sugar, hypertension, insulin, and lipids only observed in males.

Also, adjustments for the body mass index in the analysis resulted in the weakening of the area of ‘origin effect’ in males ‘for lipids’ as well as blood pressure showing that rise in such ‘factors among’ the ‘migrants’ might be caused by being obese (Hernandez et al., 2012). Besides, the migration study does not separate the effect of age during the migration starting from the period of stay in the host demography. Among the Mexicans, it had been witnessed that the ‘first generation immigrants’ had a good health irrespective of the low socioeconomic status compared to their counterparts, white Americans. However, such advantage decreases with the length of stay in America. Such findings imply that there might e latency of the effect of health behavior plus outcome. From the results of the study, there’s evidence that influence of migration on being obese is fast, happening in the initial years of ‘migration.’ This “confirms findings from migrants” to America. Hence, given a small number of migrants in the research that had lived in city centers for a short while, the results needs to be viewed with keenness plus needs ‘replication’ (Kaveeshwar and Cornwall, 2014). However, the aspect of ‘access’ to better heath care might also play a significant role in the diagnosis of high blood pressure as well as diabetes.

Besides, the subject of “migration studies” is not clear since the change in the health outcome might show the impact of an area of origin, the effect of the migration process itself, selection of the individuals who migrate as well as exposure to the new environmental factors such as climate. Mbanya et al., (2010) accounts that migration as an “exposure” is sophisticated since it entails a wide spectrum of environmental, behavioral and socioeconomic shifts. Despite the fact that data was collected using the sibling pair method, the aim of the current analysis was to compare between the urban, rural and migrant groups. Of the three groups only the migrant is paired with the rural groups while the urban group remains to be independent.  However, a recent study on India gave an adult prevalence of being obese among people in the labor force of 20% in the urban centers as well as 6% in the suburban areas. This is much lower compared to the prevalence of over 20% and 40% in rural and urban centers respectively. Besides, in the larger survey of six major towns, “the age-adjusted diabetes prevalence” of 11% was registered ‘in 2000’ (Ebrahim et al., 2010). This figure is far way low relative to the urban diabetic prevalence of 15%. In a different study in India, a diabetic prevalence of 15 % was reported. Findings from this study confirm an earlier finding of “high levels of insulin” in cities relative to the suburban ‘participants.’ This shows that a few of the impacts of rural to urban migration might be due to biological factors which lead to an increased (Yajnik, 2004).

Insulin secretion in the body because of the cells resistance. The findings presented in this study conforms to other findings  on migrants from other studies where  greater level of ‘insulin have’ been witnessed in ‘Indians living overseas,' in demographics from other developed nations going through swift rural to urban migration  as well as in migrant demographics elsewhere(Millet et al., 2013).

 The response rates presented in this study might have been lower compared to the anticipated large due to the logistic complexities of the sibling pair method. But in some of the instances, they involved say a day to reach the study station plus another day to commute back to the suburban sibling. Also, a difference in the smoking prevalence between the non-consenters, non-responders, and responders was persistent with a play of chance.

To sum it up, urbanization is connected with increased prevalence in being obese that drives other risks such as cardiovascular diseases and diabetes to mention just a few (Misra and Ganda, 2007). However, migrants and especially the ‘Indian migrants’ have sought ‘modes of life’ which place them in the same danger to the ‘urban demographics.’ Gender disparity as one of the “risk factors by place of origin is unexpected” to cause diabetes due to urbanization but it requires more explanation

References

Bowen, L., Ebrahim, S., De Stavola, B., Ness, A., Kinra, S., Bharathi, A. V., ... & Reddy, K. S. (2011). Dietary intake and rural-urban migration in India: a cross-sectional study. PloS one, 6(6), e14822.

Ebrahim, S., Kinra, S., Bowen, L., Andersen, E., Ben-Shlomo, Y., Lyngdoh, T., ... & Mohan, M. (2010). The effect of rural-to-urban migration on obesity and diabetes in India: a cross-sectional study. PLoS Med, 7(4), e1000268.

Hernández, A. V., Pasupuleti, V., Deshpande, A., Bernabé-Ortiz, A., & Miranda, J. J. (2012). Effect of rural-to-urban within-country migration on cardiovascular risk factors in low-and middle-income countries: a systematic review. Heart, 98(3), 185-19

Kaveeshwar, S. A., & Cornwall, J. (2014). The current state of diabetes mellitus in India. The Australasian medical journal, 7(1), 45.

Mbanya, J. C. N., Motala, A. A., Sobngwi, E., Assah, F. K., & Enoru, S. T. (2010). Diabetes in sub-saharan africa. The lancet, 375(9733), 2254-2266.

Millett, C., Agrawal, S., Sullivan, R., Vaz, M., Kurpad, A., Bharathi, A. V., ... & Ebrahim, S. (2013). Associations between active travel to work and overweight, hypertension, and diabetes in India: a cross-sectional study. PLoS Med, 10(6), e1001459.

Misra, A., & Ganda, O. P. (2007). Migration and its impact on adiposity and type 2 diabetes. Nutrition, 23(9), 696-708.

Miranda, J. J., Gilman, R. H., & Smeeth, L. (2011). Differences in cardiovascular risk factors in rural, urban and rural-to-urban migrants in Peru. Heart, 97(10), 787-796.

Yajnik, C. S. (2002). The lifecycle effects of nutrition and body size on adult adiposity, diabetes and cardiovascular disease. Obesity Reviews, 3(3), 217-224.

Yajnik, C. S. (2004). Early life origins of insulin resistance and type 2 diabetes in India and other Asian countries. The Journal of nutrition, 134(1), 205-210.