{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/99128"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/99128","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Essays on the relationship between public transit usage and obesity","abstract":"My dissertation consists of two essays which analyze the impact of public transit usage on obesity. Chapter 1 introduces the backgrounds of this field and layout the general framework of this thesis work. Chapter 2 conducts a cross sectional study on the impact of county population level public transit usage on obesity rates. Since the obese population may have different commuting preference in comparison to non-obese population, one can over or under estimate this effect if these preference differences are not properly controlled. This study adopts an instrumental regression approach to implicitly control for the possible selection bias due to different commuting preferences among different populations. The 2009 health data from the Behavioral Risk Factor Surveillance System (BRFSS) and transportation data from the 2009 National Household Travel Survey (NHTS) are aggregated and matched at the county level. Measures of county level public transit accessibility and vehicle ownership rates are chosen as instrumental variables to implicitly control for unobservable commuting preferences. The model suggests that a one percent increase in county population usage of public transit is associated with a 0.287 percent decrease in county population obesity rate at the alpha=0.01 statistical significance level, when commuting preferences, amount of non-travel physical activity, health resource and distribution of income are fixed. This study provides empirical support for the effectiveness of encouraging public transit usage as an intervention strategy for obesity. Chapter 3 presents a longitudinal study on this topic. Annual health data from the Behavioral Risk Factor Surveillance System (BRFSS) and transportation data from the National Household Travel Survey (NHTS) were aggregated and matched at the county level, to create a panel data set with 229 counties (from 45 states) across two time periods, 2001 and 2009. Possible confounding variables such as amount of leisure time physical activity, health care coverage and distribution of income are explicitly controlled. All time-invariant county level heterogeneities are implicitly controlled using first difference estimators. This study shows that making frequent public transit commuting possible in a county can effectively decrease the county obesity rate. Specifically, a one percent emergence of frequent public transit riders in a county population is estimated to decrease the county population obesity rate by 0.18% or more. This result supports findings in previous research that the extra amount of physical activities involved in public transit usage can have a statistically significant impact on obesity. In addition, this study also provides empirical evidence for the effectiveness of encouraging public transit usage as a public health intervention for obesity. Chapter 4 concludes this thesis work as well as postulates directions for future study.","abstract_html":"My dissertation consists of two essays which analyze the impact of public transit usage on obesity. Chapter 1 introduces the backgrounds of this field and layout the general framework of this thesis work. Chapter 2 conducts a cross sectional study on the impact of county population level public transit usage on obesity rates. Since the obese population may have different commuting preference in comparison to non-obese population, one can over or under estimate this effect if these preference differences are not properly controlled. This study adopts an instrumental regression approach to implicitly control for the possible selection bias due to different commuting preferences among different populations. The 2009 health data from the Behavioral Risk Factor Surveillance System (BRFSS) and transportation data from the 2009 National Household Travel Survey (NHTS) are aggregated and matched at the county level. Measures of county level public transit accessibility and vehicle ownership rates are chosen as instrumental variables to implicitly control for unobservable commuting preferences. The model suggests that a one percent increase in county population usage of public transit is associated with a 0.287 percent decrease in county population obesity rate at the alpha=0.01 statistical significance level, when commuting preferences, amount of non-travel physical activity, health resource and distribution of income are fixed. This study provides empirical support for the effectiveness of encouraging public transit usage as an intervention strategy for obesity. Chapter 3 presents a longitudinal study on this topic. Annual health data from the Behavioral Risk Factor Surveillance System (BRFSS) and transportation data from the National Household Travel Survey (NHTS) were aggregated and matched at the county level, to create a panel data set with 229 counties (from 45 states) across two time periods, 2001 and 2009. Possible confounding variables such as amount of leisure time physical activity, health care coverage and distribution of income are explicitly controlled. All time-invariant county level heterogeneities are implicitly controlled using first difference estimators. This study shows that making frequent public transit commuting possible in a county can effectively decrease the county obesity rate. Specifically, a one percent emergence of frequent public transit riders in a county population is estimated to decrease the county population obesity rate by 0.18% or more. This result supports findings in previous research that the extra amount of physical activities involved in public transit usage can have a statistically significant impact on obesity. In addition, this study also provides empirical evidence for the effectiveness of encouraging public transit usage as a public health intervention for obesity. Chapter 4 concludes this thesis work as well as postulates directions for future study.","abstract_has_math":false,"creators":["She, Zhaowei"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Applied Mathematics","degree_department":null,"school":null,"contributors":["Jacobson, Sheldon H.","King, Douglas M."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-02T19:59:47Z","date_published":"2018-03-02T19:59:47Z","updated_at":"2026-07-22T22:24:37Z","subjects":["Obesity","Public transit usage"],"languages":["en"],"rights":["Copyright 2017 Zhaowei She"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/99128","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jacobson, Sheldon H.","King, Douglas M."]},{"key":"dc:creator","label":"Author","values":["She, Zhaowei"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-03-02T19:59:47Z","2020-03-03T10:15:11Z","2017-07-19","2017-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Applied Mathematics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Obesity","Public transit usage"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Zhaowei She"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/99128"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["My dissertation consists of two essays which analyze the impact of public transit usage on obesity. Chapter 1 introduces the backgrounds of this field and layout the general framework of this thesis work. Chapter 2 conducts a cross sectional study on the impact of county population level public transit usage on obesity rates. Since the obese population may have different commuting preference in comparison to non-obese population, one can over or under estimate this effect if these preference differences are not properly controlled. This study adopts an instrumental regression approach to implicitly control for the possible selection bias due to different commuting preferences among different populations. The 2009 health data from the Behavioral Risk Factor Surveillance System (BRFSS) and transportation data from the 2009 National Household Travel Survey (NHTS) are aggregated and matched at the county level. Measures of county level public transit accessibility and vehicle ownership rates are chosen as instrumental variables to implicitly control for unobservable commuting preferences. The model suggests that a one percent increase in county population usage of public transit is associated with a 0.287 percent decrease in county population obesity rate at the alpha=0.01 statistical significance level, when commuting preferences, amount of non-travel physical activity, health resource and distribution of income are fixed. This study provides empirical support for the effectiveness of encouraging public transit usage as an intervention strategy for obesity. Chapter 3 presents a longitudinal study on this topic. Annual health data from the Behavioral Risk Factor Surveillance System (BRFSS) and transportation data from the National Household Travel Survey (NHTS) were aggregated and matched at the county level, to create a panel data set with 229 counties (from 45 states) across two time periods, 2001 and 2009. Possible confounding variables such as amount of leisure time physical activity, health care coverage and distribution of income are explicitly controlled. All time-invariant county level heterogeneities are implicitly controlled using first difference estimators. This study shows that making frequent public transit commuting possible in a county can effectively decrease the county obesity rate. Specifically, a one percent emergence of frequent public transit riders in a county population is estimated to decrease the county population obesity rate by 0.18% or more. This result supports findings in previous research that the extra amount of physical activities involved in public transit usage can have a statistically significant impact on obesity. In addition, this study also provides empirical evidence for the effectiveness of encouraging public transit usage as a public health intervention for obesity. Chapter 4 concludes this thesis work as well as postulates directions for future study.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-08-01","The student, Zhaowei She, accepted the attached license on 2017-07-19 at 13:19.","The student, Zhaowei She, submitted this Thesis for approval on 2017-07-19 at 13:32.","This Thesis was approved for publication on 2017-07-19 at 15:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11534 on 2018-03-02 at 13:02:43","Made available in DSpace on 2018-03-02T19:59:47Z (GMT). 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Measures of county level public transit accessibility and vehicle ownership rates are chosen as instrumental variables to implicitly control for unobservable commuting preferences. The model suggests that a one percent increase in county population usage of public transit is associated with a 0.287 percent decrease in county population obesity rate at the alpha=0.01 statistical significance level, when commuting preferences, amount of non-travel physical activity, health resource and distribution of income are fixed. This study provides empirical support for the effectiveness of encouraging public transit usage as an intervention strategy for obesity. Chapter 3 presents a longitudinal study on this topic. Annual health data from the Behavioral Risk Factor Surveillance System (BRFSS) and transportation data from the National Household Travel Survey (NHTS) were aggregated and matched at the county level, to create a panel data set with 229 counties (from 45 states) across two time periods, 2001 and 2009. Possible confounding variables such as amount of leisure time physical activity, health care coverage and distribution of income are explicitly controlled. All time-invariant county level heterogeneities are implicitly controlled using first difference estimators. This study shows that making frequent public transit commuting possible in a county can effectively decrease the county obesity rate. Specifically, a one percent emergence of frequent public transit riders in a county population is estimated to decrease the county population obesity rate by 0.18% or more. This result supports findings in previous research that the extra amount of physical activities involved in public transit usage can have a statistically significant impact on obesity. In addition, this study also provides empirical evidence for the effectiveness of encouraging public transit usage as a public health intervention for obesity. Chapter 4 concludes this thesis work as well as postulates directions for future study.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-08-01","The student, Zhaowei She, accepted the attached license on 2017-07-19 at 13:19.","The student, Zhaowei She, submitted this Thesis for approval on 2017-07-19 at 13:32.","This Thesis was approved for publication on 2017-07-19 at 15:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11534 on 2018-03-02 at 13:02:43","Made available in DSpace on 2018-03-02T19:59:47Z (GMT). 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