Why are southern who live in the southern states like Texas, Louisiana, Mississippi, Alabama, and Georgia have higher obesity rates compared to other state in different region

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Running Head: OBESITY EPIDEMIC IN SOUTH U. S STATES 1

OBESITY EPIDEMIC IN SOUTH U. S STATES 6

Obesity Epidemic Southern U. S States

Carrington Sherman

Name of Institution

Why are southern who live in the southern states like Texas, Louisiana, Mississippi, Alabama, and Georgia have higher obesity rates compared to other state in different region. Is it because of lack of education for health diets or, does it has to deal with the culture and in environment they reside in? Majority of the population who are overweight live in urban areas where there are predominantly lower income families homes (Obesity Epidemic). 

In-text citation Study design Sample size and description Independent and dependent variables Key findings Study method strengths Study method weakness Further research needs
· Conway et al.,2018). · The study design in this research is the Cohort Study · The sample size was 24,000 black Americans and 14,064 white adults sampled

· The individuals’ were of age between 40–79 years

· The samples were from underserved populations in the 12-state span that included Alabama, Florida, Arkansas, Georgia, Louisiana, Kentucky, Mississippi, South Carolina, North Carolina, Tennessee, West Virginia and Virginia.

 · The dependent variables included the racial difference in the population in U. S including the blacks Americans and the whites.

· The gaps in diabetes, cardiovascular, cancer and other chronic diseases were also dependent factor on the race, economic status and the age of the participants in the population.

· The independent variables included the races that are the African Americans and whites. Change in time was also independent because it did not rely on any factor in the population to vary but determined the extend of variation of the prevalence of the obesity, physical inactivity and diabetes

 · Risk of obesity and diabetes monotonically increased with increasing BMI and varied between blacks and whites.

· Diabetes incidences doubled in black populations than among the whites who had normal BMI.

· Racial difference became weak as the BMI increased among the populations

· The increase in the BMI remained a predominant risk factor among blacks and whites.

 · One, cohort study presents a clear temporal Sequence of outcomes against exposures over time

· Two, it allows Calculation of the incidence in terms of absolute and relative risks.

· Three, it facilitates study of rare exposures and multiple effects of an exposure. The study design will be able to single out the effect of every exposure to a factor and also combine the several effects due to every exposure on the factor that causes variation in the variables.

 · Time consuming because it involving following up a phenomenon for a long time observing and recording incidences

· It is also very expensive due to the length of time involved and the amount of data collection done.

· It is affected by many unforeseen factors in the field. As the field dynamics changes, the factors affecting the study may change and so will require new techniques or modification of the existing techniques.

· It needs studies of variables that have long latency which will enable recording of the observations consistently without termination

 · The role of treatment of obesity in checking diabetes incidence.

· Study need to be done to include individuals with multiple chronic diseases in the population.

· Sequential collection of data using uniform standardized methods and personnel its needed too to enhance data collection I cohort studies like this.

· Research question: Does the high incidences of obesity reflect on the high rate of incidence of diabetic incidence in the population of southern states of U. S?
· Lanas et al.,2016). · The study design in this research is the CESCAS I study design – is an observational and prospective cohort study · The sample size was of 7,524 women and men aged between 35 and 74 years old. They were sampled from all the 4 cities of Southern Cone of Latin America which included Bariloche and Marcos Paz in Argentina, Temuco city in Chile, and Pando-Barros Blancos in Uruguay. Marcos Paz and Pando-Barros Blancos had population size of 54,000 and 58,000 respectively, Bariloche and Temuco had 134,000 and 245,000 respectively. the study sites had been selected based on the characteristics of the population reflecting the country averages.

 · Dependent variables height, weight, sex, education, household income, occupation and healthcare access.

· Independent variables included age groups and gender. The age group varied from 35 years to 74 in the interval of five years per group.

 · Women prevalence of obesity and central obesity were higher compared to men.

· Education level increased the prevalence of obesity, physical inactivity and diabetes further.

 · It is very concrete in digging out information.

· It is comprehensive and inclusive in data collection and analysis that results to conclusion drawing and reliability of the results for generalization on the entire population

 · It needs capacity building before carrying out data collection

· It is expensive design and time consuming due to the longer time involved in data collection and tools and protocols used to collect and analyze data

 · The role of education on the increased prevalence of obesity should be studied. This will dig out the fundamental aspects of education that influence risks of obesity and how it can be used to check prevalence of obesity.

· Research question: what is the impact of increased educational level among the individuals on the incidence of obesity rates in southern states of U. S?
· Geiss et al.,2017). · The study design in this research is the Bayesian multilevel telephone survey modeling techniques and census · The sample size was 400,000 adults in 50 U. S states involving the 3143 counties. · Time was the independent variable (2004 to 2012.

· The dependent variables were prevalence of diagnosed diabetes, obesity, and individuals’ physical inactivity. These are the variables that depended on the time and the regions in the southern U. S.

 · Low median average annual percentage point changes (APPCs) of obesity, diabetes, and physical inactivity in 2008–2012 compared to 2004-2008.

· APPCs varied among counties in U. S and regions.

· APPC differences between two periods also varied in both the blacks and the whites.

 · Bayesian method can accommodate all unobserved variables and also is a powerful tool for incorporating previous information and controlling confounding.

· Census surveys increasing the confidence interval of the data collected due to large participants, it also provides a chance for identifying and attending to negative feedback.

 · Bayesian method requires in-depth understanding of the design and implementation. It is best for large computational studies.

· Census surveys limits other studies on the same population and also takes a longer time to collect information

 · There is need to be a study to estimate the effect of geographic patterns, and physical inactivity on obesity and diabetes.

· Continuous data collection systems should be set to ensure continuous standardizes data collection and storage

· Research question: does geographic variability, nutritional awareness and income status reduces the incidence of obesity significantly in southern States of U. S?

References

Conway, B. N., Han, X., Munro, H. M., Gross, A. L., Shu, X. O., Hargreaves, M. K., … & Blot, W. J. (2018). The obesity epidemic and rising diabetes incidence in a low-income racially diverse southern US cohort. PloS one, 13(1), e0190993.

Geiss, L. S., Kirtland, K., Lin, J., Shrestha, S., Thompson, T., Albright, A., & Gregg, E. W. (2017). Changes in diagnosed diabetes, obesity, and physical inactivity prevalence in US counties, 2004-2012. PloS one, 12(3), e0173428.

Lanas, F., Bazzano, L., Rubinstein, A., Calandrelli, M., Chen, C. S., Elorriaga, N., … & Poggio, R. (2016). Prevalence, Distributions and Determinants of Obesity and Central Obesity in the Southern Cone of America. PloS one, 11(10), e0163727. Retrieved from: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0163727

 
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