Effects of Fast Food on BMI Level in Children - Research Paper Example

Effects of Fast Food on BMI Level in Children 1.0 Introduction High BMI rates among American adolescents have tripled in the past few years. Recent approximations demonstrate that 17.6% of U.S youths aged between 12 to 19 years are obese. Along with the increasing overweight epidemic, research information on food intake cycle and family spending indicate a marked up pattern in the whole energy consumption acquired from far, from dwelling sources, specifically fast food restaurants (Pinstrup-Andersen & Cheng 2009, p. 107). Youths eat the biggest percentage of calories far from abode at quick service outlets, and on top of affecting household food acquisitions, they represent a significant customer market since they are much more probable that adolescents to be straight customers. U.S youths are approximated to have used $159 billion during 2005. Moreover, the most regularly witnessed food item advertisements by youths at the age of 12 to 17 are fast food (Heshmat 2011, p. 58). 2.0 Body Many researches have studied the relationships between fast food intake and energy and nutrient consumption and body mass results (Schlosser 2012, p. 111). Fast food intake has been related to greater total energy consumption and greater consumption of fat, carbohydrates, carbonated soft drinks, sugar, saturated fat and lesser consumption of micronutrients and vegetables and fruits. In addition, researchers have discovered important relationships between fast food intake and raised BMI, raised body mass, and a greater likely of being obese (Ludwig & roster 2007, p. 87). The reducing actual cost of food and the comparatively low price and expediency of energy rich foods, specifically, are imagined like major contributors to obesity. Just a constrained number of researches, nevertheless, have studied the degree to which economic methods associated with energy rich fast food intake like cost and accessibility are associated with body mass results and the majority of these researches has applied cross-sectional information. Greater fast food costs and food at abode costs have been associated with reduced body mass results among grown- ups applying cross-sections of the 1984 to 1999 Behavioural Risk Factor Surveillance System (Ferry 2011, p. 96). Greater fast food costs have been associated with reduced BMI and overweight among youths applying cross-sections since 1997 to 2003 of the Monitoring the Future information, 2001 to 2003 cross-sections of the Medical Expenditure Panel Survey and 1997 to 1999 cross-sections of the National Longitudinal Survey of Youth 97 (NLSY97) (Moreno Aznar, bigots & Ahrens 2011, p. 148). Research outcomes founded on random results models have discovered that reforms in child mass are mathematically optimistically importantly associated with the cost of fruit and vegetables, however, not to the reforms in fast food costs. These researches apply longitudinal information from the Early Childhood Longitudinal Research on youths, pursued from kindergarten up to third grade and past fifth grade; and applying information from the adolescents of the National Longitudinal Survey of Youth 1979 (Centre for Educational Research And Innovation 2010, p. 144). Despite the latter research discovered that fast food costs were mathematically importantly associated with reduced BMI when the sample was limited to aged youths, 13 to 17 old. The association between fast food outlet accessibility and child or youth mass results has not been discovered to be mathematically important; despite discovering bigger numbers of accessible total outlets to be importantly related with greater adult overweight (Kornblum & Smith 2008, p. 546). This research studies the association between teenager BMI and fast food costs and fast food outlet accessibility applying panel information approximation techniques (Galea 2007, p. 322). Specifically, unobserved personal level heterogeneity is reported for though approximating personal level fixed results replicas. The breakdown in addition organizes for extra neighbourhood related elements including universal food costs and the accessibility of filling service outlets, superstores, grocery hoards, expediency stores, and business physical exercise associated amenities. In sensitivity breakdown, confined region median family revenue as well is managed. This research draws on four waves of the 1997 National Longitudinal Survey of Youth (NLSY97) to study the determinants of teenagers BMI. Food cost and restaurant concentration information are connected to the NLSY97 by decade and county echelon geocode identifiers (Kopelman, Caterson & Dietz 2010, p. 448). Applying cross-sectional approximation techniques, the results prove outcomes from before cross-sectional researches that the cost of fast food possesses a statistically important weak negative association with teenage BMI. Managing of personal level constant results, a statistically important negative association between fast food costs and mass is discovered, however, the effect dimension is reduced than the cross-sectional approximate. Studying cost awareness through parental revenue and education, there is proof that the body mass of adolescents in low to average socioeconomic condition households is majorly responsive to fast food costs (Bagchi 2011, p. 505). 3.0 Data This research combines personal level panel information from the NLSY97 on teenagers with outside information on fast food costs and common food costs acquired from the American Chamber of Commerce Researchers Association (ACCRA) and restaurant, density information acquired from commercial lists established through Dun and Bradstreet (D&B) (Culyer 2013, p. 40). The outside food cost and restaurant concentration procedures are equated to the personal level information applying county echelon geocode identifiers for every NLSY97 board gesture from 1997 past 2003. Population information applied to produce per capita restaurant concentration measures and information on median family revenue applied in awareness breakdown are concluded from Poll 2000 (Kushner & Bessesen 2007, p. 409). 3.1 NLSY97 panel data This research applies four waves (1997 all the way to 2000) of the NLSY97 where interviewees were in the bracket of 12 to 17 years of age during 1997 (Maholmes & King 2012, p. 266). This research is restrained to teenagers who stay at abode. To view to parental data, information on parental revenue was accounted through parents in the total four gestures and is acquired from the 1997 parent survey and the 1998 to 2000 family revenue update survey. Information on mother’s learning and job condition is acquired from the 1997 parent survey and in successive decades was accounted for through teens in the yearly family roster information. On the grounds of the non-missing information, the approximation sample constitutes 11, 900 individual decade surveillances on an unstable board of 5215 people staying in 392 diverse nations all over the U.S. The major result gauge in this research is BMI. BMI is computed founded on the personal accounted for anthropometric data accessible in the NLSY97 study. BMI is computed like similar to mass (kilogram) /height (meter) squared (Dunn 2008, p. 25). Teenagers were grouped as obese when BMI ≥ age, gender, particularly 95th percentile founded on the CDC development graph. Personal and family echelon control features constitute race, age, sex, teenage revenue (which constitutes earned revenue and grant from blood relations), and household arrangement (teen adult stays with one parent against both parents). Moreover, teens yearly hours of the job, parental revenue, mother learning (less than secondary, secondary, some college and further), mother’s job condition (not doing the job, doing job part time, doing jobs full time) and countryside, suburban and city pointers (Wendt & Todd 2011, p. 14). 3.2 Fast food price and fast food restaurant availability measures Fast food cost information is acquired from the ACCRA Cost of Living Index accounts. These accounts possess quarterly data on costs all over more than three hundred U.S towns. These cost information is compared to the NLSY97 model founded on the nearest town match accessible in the ACCRA information applying the county echelon geocode identifier. Observations for which cost comparisons were not accessible from similar or contiguous county were not constituted in the breakdown. Moreover, a cost match pointer was constituted in the approximation to manage cost matches founded on a contiguous, against precise county match (Grossman & Macon 2011, p. 141). A fast food cost directory was produced founded on the below three products constituted in the ACCRA information. A McDonald Quarter Pounder with cheese, a thin crust, regular cheese pizza at Pizza Hut and/ or Pizza Inn, and fried chicken (thigh and drumstick) at Kentucky Fried Chicken and/ or Church Fried Chicken is included. The cost directory is measured based on the used shares generated through ACCRA obtained from the Bureau of Labour Statistics (BLS) Consumer Price Index (CPI). The fast food shop outlet concentration gauge is established applying information got from commercial lists established through D&B below the 4digit categorization of eating areas. Fast food shops are explained through the filled up combination of key 8 digit SIC codes that fall below fast food inns and shops exempting coffee outlets and constituting the pair of key 8 digit SIC systems for series and sovereign pizzerias (Cawley 2011, p. 653). 4.0 Conclusion Due to the rising rates of teenage obesity, legislators move on reviewing several policy interferences to turn around this route and fast food levies include such tools. Teenagers make important food buys of their own and fast food usage is well inside the monetary reach of several youths even if it is not bought for them straight through their parents. Moreover, youths on their own are not probable to know the nutritional and wellbeing effects of their food selections and they can have great percentages of time predilection. Adolescence is a significant evolution era as food usage blueprints start to become further everlasting and youth obesity itself trails into maturity. Several cross-sectional researches and a single longitudinal random implications research have discovered statistically important negative relationships between fast food costs and teenager’s BMI and/ or obese prevalence proposing that fast food taxes can be an efficient instrument for overcoming extreme fast food usage and associated obesity. Applying cross-sectional approximation techniques, this research as well will discover that youth BMI is sensitive to the cost of fast food. The aim of this research is to assess if results founded on cross-sectional approximations are the same observed when applying panel information to manage unobserved personal level heterogeneity. Applying a personal level constant implications approximated outcomes show that greater fast food costs are statistically importantly associated with reductions in teenage BMI. The fast food cost elasticity for BMI is 0.08 according to the fixed effects replica equated to -0.10 according to the OLS replica proposing that the cross-sectional approximations over-approximate the cost awareness through about 25%. Moreover, there are several noteworthy results in this research with respect to household level features. The negative association between parental revenue and teenage mass present in the cross-sectional breakdown is not discovered to be present in the board information approximations. Reporting for unnoticed personal level heterogeneity, the approximations showed that reforms in parental revenue have efficiently zero influence on reforms in youth weight. Likewise, the constant effects approximations did not show any important impacts of staying in a single against a pair parent household. This research did not discover an optimistic association between mother’s hours of job and teen’s BMI. Actually, part time maternal job condition against not doing the job is statistically importantly associated with reduced adolescence BMI and full time job against not doing the job had a feebly statistically important negative impact. These results are diverse to before board approximations for teenagers that discovered that bigger hours of maternal job are associated with rising teen obesity. This research will propose that these impacts do not continue into the teenage years. In brief, reporting for personal level unnoticed heterogeneity, the outcomes from the personal level fixed impacts replica proof the cross-sectional results that greater fast food costs are anticipated to lower youth BMI. In general, nevertheless, the modest cost elasticities even though bigger for the low-to average-SES inhabitants propose that fast food levies would have to stay considerable for them to have any remarkable impact on body mass. Extra longitudinal researches are required in order to precisely figure out the degree to which people can anticipate the beginning of new fast food levies to importantly lower teenage body mass. Specifically, longitudinal researches that have the capacity to manage for the local food, atmosphere at more close geographic levels like the zip code or poll tract level could contribute heavily to the proof foundation. Childhood overweight is a key public health matter and is currently getting a great deal of keenness because of its wider economic effects and extended impacts on teen’s general health, academic achievements, quality of living, and productivity as they turn to mature people. Luckily, the major current statistics shows that increase in the percentage of teens grouped as obese has eventually leveled off. Nevertheless, the proportions of obese and children with high body mass remain elevated. This condition is directly associated with the increase in fast food restaurants from home which are easily accessible and cost effective for children to afford even when their parents do not directly purchase the products for them. Hence, something needs to be done to curb the situation before it gets out of hand. Bibliography Bagchi, D 2011, Global perspectives on childhood obesity current status, consequences and prevention. London, Academic. Cawley, JH 2011, The Oxford handbook of the social science of obesity. New York, Oxford University Press. Centre for Educational Research And Innovation, 2010, Improving health and social cohesion through education. [Paris], OECD, Centre for Educational Research and Innovation. Culyer, AJ 2013, Encyclopedia of health economics. Burlington, Elsevier Science. Dunn, RA 2008, Three essays on the economic determinants of household meal production and eating behavior. Thesis (Ph.D.)-- University of Wisconsin--Madison, 2008. Ferry, RJ 2011, Management of pediatric obesity and diabetes. New York, Humana Press. Galea, S 2007, Macrosocial determinants of population health. New York, NY, Springer. Grossman, M & Mocan, HN 2011, Economic aspects of obesity. Chicago, The University of Chicago Press. Heshmat, S 2011, Eating behavior and obesity: behavioral economics strategies for health professionals. New York, Springer Pub. Co. Kopelman, PG Caterson, ID & Dietz, WH 2010, Clinical obesity in adults and children. Chichester, West Sussex, Wiley-Blackwell. Kornblum, W & Smith, CD 2008, Sociology in a changing world. Belmont, CA, Thomson/Wadsworth. Kushner, RF & Bessesen, DH 2007, Treatment of the obese patient. Totowa, N.J., Humana. Ludwig, D & Rostler, S 2007, Ending the food fight: guide your child to a healthy weight in a fast food/fake food world. Boston, Houghton Mifflin. Maholmes, V & King, RB 2012, The Oxford handbook of poverty and child development. New York, Oxford University Press. Moreno aznar, L, Pigeot, I, & Ahrens, W 2011, Epidemiology of obesity in children and adolescents: prevalence and etiology. New York, Springer. Pinstrup-Andersen, P, & Cheng, F 2009, Case studies in food policy for developing countries. Ithaca, N.Y., Cornell University Press. Schlosser, E 2012, Fast food nation: the dark side of the all-American meal. Boston, Mariner Books/Houghton Mifflin Harcourt. Wendt, M & Todd, JE 2011, The effect of food and beverage prices on children's weights. [Washington, D.C.], U.S. Dept. of Agriculture, Economic Research Service. Read More