This document summarizes a study that aims to improve an existing econometric model for estimating the causal impact of obesity on medical costs. The authors successfully replicate the original study's two-part model, which uses the BMI of a respondent's oldest child as an instrumental variable. They then make several modifications, including expanding the sample period, adding control variables for health status and insurance, and investigating potential time heterogeneity. The results suggest that the original study may have overestimated obesity's effect, with the marginal cost of obesity decreasing from $3,297 to $1,956 after controlling for additional health factors.
Healthy Food Accessibility and Obesity: Case Study of Pennsylvania, USArsmahabir
Abstract-Obesity is a continuing challenge for any town, city or country faced with this problem. Being obese increases your risk of physical disorders such as high blood pressure (BP), high blood cholesterol, diabetes, coronary heart disease, stroke, cancer and poor reproductive health. Higher obesity rates also leads to increased economic burden on society. In order to better understand and control obesity rates the in uence of various factors on its prevalence should be investigated. We used Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models to analyze spatial relationships using a combination of socio-economic and physical factor for counties in Pennsylvania (PA), USA for 2010. Our ndings suggest that the rate of obesity is impacted by local spatial variation and its prevalence positively correlated with diabetes, physical inactivity and the distance that a person must travel to get to a healthy food store. Additionally, GWR (AICc = 261.59; r-squared = 0.45) was found to signi cantly improve model tting over OLS (AICc = 299.87; r-squared = 0.34). These results indicate that additional factors, including social, cultural and behavioral, are needed to better explain the distribution of obesity rates across PA.
Data is an essential commodity and various organizations today unlock data to allow them to make business decisions that are highly informed. Data in open source has become highly available and U.K Government has a wide range of available open data to analyse. The paper of this report lies in information extraction from data sets of health for supporting development for wide range of food products that are healthy. The scope of this paper lies in analysing and extracting information from distinct data sets using a specific tool of data analytics that is either SAS JMP or SAS Enterprise guide or base SAS. After this analysis, results for the data will be analysed for showing the requirement for a wide range of food products that are healthy.
Expositor: Juan Ponce -Director FLACSO Ecuador
Seminario Internacional sobre Experiencia exitosas en Nutrición, organizado por el Programa Mundial de Alimentos de las Naciones Unidas (PMA) en Colombia y DSM.
14 y el 15 de mayo de 2015.
Bogotá, Colombia.
Healthy Food Accessibility and Obesity: Case Study of Pennsylvania, USArsmahabir
Abstract-Obesity is a continuing challenge for any town, city or country faced with this problem. Being obese increases your risk of physical disorders such as high blood pressure (BP), high blood cholesterol, diabetes, coronary heart disease, stroke, cancer and poor reproductive health. Higher obesity rates also leads to increased economic burden on society. In order to better understand and control obesity rates the in uence of various factors on its prevalence should be investigated. We used Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models to analyze spatial relationships using a combination of socio-economic and physical factor for counties in Pennsylvania (PA), USA for 2010. Our ndings suggest that the rate of obesity is impacted by local spatial variation and its prevalence positively correlated with diabetes, physical inactivity and the distance that a person must travel to get to a healthy food store. Additionally, GWR (AICc = 261.59; r-squared = 0.45) was found to signi cantly improve model tting over OLS (AICc = 299.87; r-squared = 0.34). These results indicate that additional factors, including social, cultural and behavioral, are needed to better explain the distribution of obesity rates across PA.
Data is an essential commodity and various organizations today unlock data to allow them to make business decisions that are highly informed. Data in open source has become highly available and U.K Government has a wide range of available open data to analyse. The paper of this report lies in information extraction from data sets of health for supporting development for wide range of food products that are healthy. The scope of this paper lies in analysing and extracting information from distinct data sets using a specific tool of data analytics that is either SAS JMP or SAS Enterprise guide or base SAS. After this analysis, results for the data will be analysed for showing the requirement for a wide range of food products that are healthy.
Expositor: Juan Ponce -Director FLACSO Ecuador
Seminario Internacional sobre Experiencia exitosas en Nutrición, organizado por el Programa Mundial de Alimentos de las Naciones Unidas (PMA) en Colombia y DSM.
14 y el 15 de mayo de 2015.
Bogotá, Colombia.
Is Technological Change In Medicine Always Worth It? The Case Of Acute Myoc...Vibha Amblihalli
Health Economics Class activity: Article presentation
Analysis of healthcare spending and one year survival following heart attack in the United States between 1986 and 2003.
Jack Wennberg on unwarranted variation in medical practice - lessons from the...The King's Fund
Dr Jack Wennberg, founder and director of the Dartmouth Institute for Health Policy and Clinical Practice, and founding editor of the Dartmouth Atlas of Health Care, gives his perspective on the challenges faced by the health system in England in reducing unwarranted variation.
Kaouthar lbiati-health-composite-indicators as measures for equityKaouthar Lbiati (MD)
There is no consensus regarding conditions and circumstances where each individual rank-dependant indicator of socio-economic inequality is to be used. What emerges from this paper is that the concentration index approach needs to be confined to situations where the health variable is of ratio-scale type.
Mathematical Model for Drug Therapy in Patients With Diabetes MellitusIJESM JOURNAL
This study presents a new mathematical model for Drug Therapy in Patients with Diabetes
Mellitus which includes external rate at which blood glucose, insulin and epinephrine is being
increased in the form, ( , , ) ( )
.
Y f g h e r t i i . The system has been analyzed and solved to provide
the systems natural frequency, ω0, which is the basic descriptor of saturation level of the drug.
We establish that the resonance period for the final model, that is, T0=3.76912 hrs, agrees well
with the data for the existing insulin therapy, showing that the peak, which is the time period for
insulin to be most effective in lowering blood sugar, is in the acceptable therapeutic range.
Mathematics Subject Classification: Primary 93A30; Secondary 91B74, 93C15, 92C50, 92C42
Food intake and the environment are the two major factors that affect the health or illness of an individual. Studies in nutritional area have increased the understanding of how to maintain healthy groups of individuals that live in different dietary conditions. After the conclusion and assessment of the Human Genome Project (HGP), new insights about the influence of nutrients into people’s diet were postulated, which included, Some examples of this gene-nutrient interaction are their capacity on binding to the main transcription factors(Cahill et al.,2003). This binding enhances or interferes with the ability of transcription factors on interacting with elements that will pave to the binding control of RNA polymerase. Interactome also have a major role to play in nutrigenomics (Sreeremya, 2018)
This paper investigates the non-pecuniary benets of education using several individuals' health outcomes, health-damaging and health-improving behaviors, and preventive care. We exploit a reform which raised compulsory schooling by three years in Italy to identify the causal effect of lower secondary education and, unlike most previous papers in the literature, we analyze a wide range of health indicators. Our analysis shows that the rise in schooling induced by the reform reduced BMI and the incidence of obesity across Italian women, and raised men's likelihood of
doing regular physical activity and cholesterol and glycemia checks. No effect is found instead on preventive care and health-improving behavior for women, and on smoking prevalence and intensity for both genders. Some potential reasons for the gender differences in the results are discussed.
This draft: December 16, 2016
Preliminary and Incomplete. Please do not cite.
Is Technological Change In Medicine Always Worth It? The Case Of Acute Myoc...Vibha Amblihalli
Health Economics Class activity: Article presentation
Analysis of healthcare spending and one year survival following heart attack in the United States between 1986 and 2003.
Jack Wennberg on unwarranted variation in medical practice - lessons from the...The King's Fund
Dr Jack Wennberg, founder and director of the Dartmouth Institute for Health Policy and Clinical Practice, and founding editor of the Dartmouth Atlas of Health Care, gives his perspective on the challenges faced by the health system in England in reducing unwarranted variation.
Kaouthar lbiati-health-composite-indicators as measures for equityKaouthar Lbiati (MD)
There is no consensus regarding conditions and circumstances where each individual rank-dependant indicator of socio-economic inequality is to be used. What emerges from this paper is that the concentration index approach needs to be confined to situations where the health variable is of ratio-scale type.
Mathematical Model for Drug Therapy in Patients With Diabetes MellitusIJESM JOURNAL
This study presents a new mathematical model for Drug Therapy in Patients with Diabetes
Mellitus which includes external rate at which blood glucose, insulin and epinephrine is being
increased in the form, ( , , ) ( )
.
Y f g h e r t i i . The system has been analyzed and solved to provide
the systems natural frequency, ω0, which is the basic descriptor of saturation level of the drug.
We establish that the resonance period for the final model, that is, T0=3.76912 hrs, agrees well
with the data for the existing insulin therapy, showing that the peak, which is the time period for
insulin to be most effective in lowering blood sugar, is in the acceptable therapeutic range.
Mathematics Subject Classification: Primary 93A30; Secondary 91B74, 93C15, 92C50, 92C42
Food intake and the environment are the two major factors that affect the health or illness of an individual. Studies in nutritional area have increased the understanding of how to maintain healthy groups of individuals that live in different dietary conditions. After the conclusion and assessment of the Human Genome Project (HGP), new insights about the influence of nutrients into people’s diet were postulated, which included, Some examples of this gene-nutrient interaction are their capacity on binding to the main transcription factors(Cahill et al.,2003). This binding enhances or interferes with the ability of transcription factors on interacting with elements that will pave to the binding control of RNA polymerase. Interactome also have a major role to play in nutrigenomics (Sreeremya, 2018)
This paper investigates the non-pecuniary benets of education using several individuals' health outcomes, health-damaging and health-improving behaviors, and preventive care. We exploit a reform which raised compulsory schooling by three years in Italy to identify the causal effect of lower secondary education and, unlike most previous papers in the literature, we analyze a wide range of health indicators. Our analysis shows that the rise in schooling induced by the reform reduced BMI and the incidence of obesity across Italian women, and raised men's likelihood of
doing regular physical activity and cholesterol and glycemia checks. No effect is found instead on preventive care and health-improving behavior for women, and on smoking prevalence and intensity for both genders. Some potential reasons for the gender differences in the results are discussed.
This draft: December 16, 2016
Preliminary and Incomplete. Please do not cite.
es una herramienta genial para encontrar ese canal rápidamente y sin ninguna dificultad y está disponible para Android de forma gratuita tanto en versión para smartphone como para tablet.
May 20 2015 at the ASX - Presentations at the Emerging Markets & Disruptive Technology: New Horizons in Corporate Governance Symposium
UTS Corporate Governance Services Program
External validation of an electronic phenotyping algorithm to detect attentio...TÀI LIỆU NGÀNH MAY
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Unit 5Instructions Enter total points possible in cell C12, under.docxmarilucorr
Unit 5Instructions: Enter total points possible in cell C12, under the rubric. Next enter scores (between 0 and 4) into yellow cells only in column F.Evidence-Based Clinical Question SearchUnsatisfacotrySatisfactoryAverageExcellentScoreWeightFinal Score1234Identify your refined PICOT question.IncompleteN/AN/AComplete05%0.00Using PubMed and the Cochrane collaboration database, do a systematic review of your clinical question.IncompleteN/AN/AComplete010%0.00Describe your systematic review and include an errors analysis.IncompleteN/AN/AComplete010%0.00Determine an evidence-based quantitative article from the search that contains an evidence-based randomized control trial.IncompleteN/AN/AComplete010%0.00Summarize the case study selected.IncompleteN/AN/AComplete05%0.00Describe the study approach, sample size, and population studied.IncompleteN/AN/AComplete05%0.00Apply the evidence from this review to your practice specifically in your overview.IncompleteN/AN/AComplete010%0.00Evaluate the outcomes, identifying the validity and reliability.IncompleteN/AN/AComplete010%0.00Discuss if the study contained any bias.IncompleteN/AN/AComplete010%0.00Determine the level of evidence identified in the review.IncompleteN/AN/AComplete010%0.00LengthLess than 7 pages8 pages9 pages10 pages 05%0.00Format/StyleDid not follow APA formatMajor errors with APA formattingText, title page, and references page follow APA guidelines . Minor references and grammar errorsText, title page and references page follow APA guidelines. No grammar, word usage or punctuation errors. Overall style is consistent with professional work.010%0.00100%0.00Final Score0Percentage0.00%Total available points =2504Rubric ScoreGrade pointsPercentageLowHighLowHighLowHigh3.54.022525090%100%2.53.4920022580%89.99%1.72.4917520070%79.99%1.01.6915017560%69.99%0.01.000150059.99%Comments:
Running head: EFFECTIVE OBESITY MANAGEMENT 1
EFFECTIVE OBESITY MANAGEMENT 2
Effective Obesity Management
Kaplan University
Topic Selection 42/42
EBP Overview 48/48
Length 6/6
Format/Style 20/24
Total 116/120 Outstanding paper with APA errors.
Introduction
With this paper, I will share my research topic question, after much consideration, I came across the best way to address the question and yield evidence based practice results. My question is: is bariatric surgery effective in yielding long term success when compared to lifestyle changes, in the obese population? I will also include the use of reputable reliable search resources such as Cochrane Database. There are significant healthcare issues that can be addressed within my area of specialty, and the focus in healthcare should be to create a better environment where best healthcare decisions can be made. The understanding of these healthcare issues provides a better environment where better pr ...
IntroductionSeveral economic types of research have demonstrat.docxnormanibarber20063
Introduction
Several economic types of research have demonstrated that there is a strong positive correlation between years of schooling and health. However, the main question centered in this study is the relationship that exists between education and Health (Buckles, et al.2013). This paper will employ several changes that have been made in education and health studies to test the hypothesis that there is a causal relationship between education and health. Results from this study suggest that there is a causal relation ranging from more schooling to better health, which is more significant than the standards regression suggestions
Description
Public intellectuals and policymakers usually emphasize the essence of education. They argue that education results in expanded job opportunities and higher expected earnings. However, there may be other essential benefits of education, which have not been understood appropriately. Recent economic literature reviews on the effects of education on the health of a population found out that there is substantial evidence that links education not only to increase earning potential of an individual but also to reduce criminal behavior. This is also related to increased voting as well as democratic participation and improved health outcomes. Given the fact that education is a crucial multifaceted component that affects health; the research composed in this paper has education and health policy makers, as its targets audiences due to the multiple causative relationships between the two variables. The ability of policymakers and the governments to understand the Education- Health relationship would help them whenever deciding on whether to invest more in education or healthcare.
.
Literature Review
With the current empirical economics, hypotheses usually go either way, depending on the economist’s perspective. One might assume that better education leads to better health or better health lead to a better education. Or maybe the fact that education brings more income thus betters health; versus better health helping individuals become more educated. But one thing that we could all agree on is the fact that education correlates with health. Education is one of the major social factors that most economic researchers have cited that is linked to longer lifespans in every country where it has been studied. For example; according to the CDC: for every 100,000 deaths amongst non-high school graduate American males aged between 25 to 64 years old, the mortality rate was 655.2; for the males within the same age group but with high-school diplomas, the mortality rate is 600.9. Whereas; the mortality rate for those with college education or higher given the same parameters was 238.9(Martinek, 2017). Such results are a pure reflection of the fact that the more educated people are, the more likely they are better informed thus making better health choices.
Alternatively, health in young adulthood and childhood years may .
This article is a departure from many prior studies in the literature on Medicare spending in the United
States. Previous works have focused on time-invariant or hereditary demographic characteristics and
congenital health status. In contrast, this study examined state-level variations in Medicare costs per
enrollee with special emphasis on prominent acquired health-related lifestyle attributes that are more
reversible over a short time period. Our main findings are (1) reversible acquired health-related lifestyle
attributes such as smoking and obesity are statistically significant determinants of state-level variations in
Medicare costs; and (2) state-level variations in Medicare spending is elastic with respect to changes in the
prevalence of the two acquired health-related lifestyle attributes.
Community ProblemThe community issue addressed is the high preva.docxtemplestewart19
Community Problem
The community issue addressed is the high prevalence rates of obesity and overweight. In this regard, the challenge is comprehensive, owing to categorizing the aspect as a lifestyle condition. Subsequently, other factors, such as nutrition, inadequate physical exercise, and sedentary lives contribute to the issue. The problem is significant, owing to substantial correlations between obesity, overweight, and other comorbidities. The implication is that obesity is a risk factor for other illnesses, including cardiovascular diseases, obesity, cancer, and other issues. In such a case, programs and initiatives implemented to reduce prevalence should be adequate. Accurate evaluation is critical in attaining the best outcomes, including follow-up, adherence, and addressing elements that require a change to meet emerging needs.
Structure
The evaluation structure follows a pre-and post-intervention approach. In this regard, the emphasis is on the initiatives and their ability to meet the set goals. According to the CDC (2016), obesity evaluation measures often employ baseline data to compare progress at the post-implementation phase. In this regard, the structure entails collecting baseline data of the metrics, such as BMI, waistline, and weight, among other anthropometric factors. After the intervention, such as a community education program sensitizing users on the risk factors associated with obesity and overweight, the evaluation will compare the baseline measures to assess any progress. To illustrate, evaluating how the BMI changed after a participant implements recommended steps will help determine efficacy. As a result, the suggested structure focuses on a pre-and post-intervention approach.
Process
The evaluation process will be goal-based. Subsequently, the procedure will focus on specific objectives determined by the set metrics. According to Seral-Cortes et al. (2021), an effective evaluation process should emphasize knowing the goals and project outcomes, testing them against set results. Additionally, precise objectives and measurable data are also vital in promoting an effective process of assessment. Other components or steps incorporate using a logic model to describe the intervention or program, formulating the project's acceptability criteria, and developing required questions. In the proposed process, a goal-based method will apply. Subsequently, post-intervention, goals will be formulated or indicators of success, such as reducing the prevalence levels by 25% in the first three months. Behavioral changes, including nutritional awareness assessed by selecting at least three healthy diets after four weeks of community education, will be helpful.
Outcome Standards
The outcomes will focus on behavior and prevalence levels in the long-term from the example of community education and awareness. As described, after three months, disease prevalence at the community level will reduce by 25%. Additionally, behavioral.
Community ProblemThe community issue addressed is the high preva.docxjanthony65
Community Problem
The community issue addressed is the high prevalence rates of obesity and overweight. In this regard, the challenge is comprehensive, owing to categorizing the aspect as a lifestyle condition. Subsequently, other factors, such as nutrition, inadequate physical exercise, and sedentary lives contribute to the issue. The problem is significant, owing to substantial correlations between obesity, overweight, and other comorbidities. The implication is that obesity is a risk factor for other illnesses, including cardiovascular diseases, obesity, cancer, and other issues. In such a case, programs and initiatives implemented to reduce prevalence should be adequate. Accurate evaluation is critical in attaining the best outcomes, including follow-up, adherence, and addressing elements that require a change to meet emerging needs.
Structure
The evaluation structure follows a pre-and post-intervention approach. In this regard, the emphasis is on the initiatives and their ability to meet the set goals. According to the CDC (2016), obesity evaluation measures often employ baseline data to compare progress at the post-implementation phase. In this regard, the structure entails collecting baseline data of the metrics, such as BMI, waistline, and weight, among other anthropometric factors. After the intervention, such as a community education program sensitizing users on the risk factors associated with obesity and overweight, the evaluation will compare the baseline measures to assess any progress. To illustrate, evaluating how the BMI changed after a participant implements recommended steps will help determine efficacy. As a result, the suggested structure focuses on a pre-and post-intervention approach.
Process
The evaluation process will be goal-based. Subsequently, the procedure will focus on specific objectives determined by the set metrics. According to Seral-Cortes et al. (2021), an effective evaluation process should emphasize knowing the goals and project outcomes, testing them against set results. Additionally, precise objectives and measurable data are also vital in promoting an effective process of assessment. Other components or steps incorporate using a logic model to describe the intervention or program, formulating the project's acceptability criteria, and developing required questions. In the proposed process, a goal-based method will apply. Subsequently, post-intervention, goals will be formulated or indicators of success, such as reducing the prevalence levels by 25% in the first three months. Behavioral changes, including nutritional awareness assessed by selecting at least three healthy diets after four weeks of community education, will be helpful.
Outcome Standards
The outcomes will focus on behavior and prevalence levels in the long-term from the example of community education and awareness. As described, after three months, disease prevalence at the community level will reduce by 25%. Additionally, behavioral.
Classifying Readmissions of Diabetic Patient EncountersMayur Srinivasan
Readmission rates in hospitals are a key indicator on quality of patient care and a clear indication of total cost or inconvenience related to the treatment. Patients with serious medical
conditions such as diabetes mellitus are key drivers of readmission rates owing to the complexity of their illness. Therefore, being able to predict based on certain features whether or not a patient
will need readmission can help doctors and hospitals provide better care initially and not get financially penalized under Obamacare’s readmission policy
Take a look at this dnp capstone project sample and discover what is correct format of it. FOr more info check this site. https://www.capstonepaper.net/our-capstone-papers/capstone-nursing-paper-writing-services/
Can Post-Stratification Adjustments Do Enough to Reduce Bias in Telephone Sur...
metrics_game_paper
1. Metrics Game Submission
Enoch Chan
Neil Cho
Yuan Fei
Jessica Koh
May 15, 2015
1 Introduction
Cawley and Meyerhoefer (2012) use instrumental variables to estimate the impact of obesity on
medical costs. Their structural model of medical spending is a two-part model that looks at the
marginal effect of obesity on healthcare costs. The study uses the eldest child’s Body Mass Index
(BMI) as an instrumental variable for the individual’s BMI due to the inherent genetic link to
correct for endogeneity and attenuation bias. In this report we are able to replicate both their
main non-IV and IV results while further extending the results of their baseline model using newer
data. Additionally, we address possible limitations of endogeneity in the instrument and issues of
underestimation due to the selective nature of our sample.
2 Data
Cawley and Meyerhoefer (C&M hereafter) use Medical Expenditure Panel Survey (MEPS) data
from 2000 to 2005 to estimate obesity’s implications. They limit their sample to adults between the
ages of 20 to 64 with biological children between the ages of 11 and 20, excluding pregnant women
and possible outliers. We apply the same criteria to the publicly-available MEPS data in order to
first replicate their findings. We then expand our sample in order to improve upon existing results
in the literature. In particular, we look at healthcare cost data from 2000 to 2012 normalized to
2005 dollars.
1
2. 3 Model Replication
3.1 Model Description and Implementation
The two-part model of C&M consists of: 1) a logit model estimating the probability that a respondent
spends positive medical expenditure, and 2) a Gamma GLM with a log link estimating the amount
that a respondent spends conditional on spending a nonzero amount. The independent variable
of interest is either the BMI level or a dummy variable indicating obesity. They instrument this
variable with the BMI level or obesity indicator of the adult’s eldest child. They control for various
factors, specifically: gender, ethnicity, age (indicator variables for whether age in years is 20-34,
35-44, 45-54, 55-64), education level, census region (northeast, midwest, south, or west), MSA status,
employment, household composition (average age of all household members) and fixed effects for
year.
We are able to able implement a two-part model as above. As in C&M, we check the validity of the
instruments by using an F-test, and we find that all instruments are valid. In order to replicate
their instrumental variable results, we first regressed our endogenous variable (respondents’ BMI or
obesity) on the instruments and controls. We then used the predicted value of respondents’ BMI or
obesity from this regression as our independent variable of interest in the two-part model. We use
robust standard errors for the regression and cluster them by the primary sampling unit on the
family level.
C&M include a second-degree polynomial of their instrument (eldest child’s BMI) in their two-part
regression. We incorporate this nonlinearity through the mechanics of our first regression, i.e. we
regress respondents’ BMI on a second-degree polynomial of eldest child’s BMI and then use the
predicted value as an independent variable in the two-part model. Our results are summarized in
Tables 1 and 2.
3.2 Replication Results
Table 1 reports the point estimates from both stages (Logit and GLM) of our replication using
BMI and an indicator of obesity as alternative measures of obesity. Both IV and non-IV results are
reported.
Table 2 reports the marginal effect estimates from both stages of our replication with both measures
of obesity. The marginal effect is defined as the estimated change in medical expenditure per unit
change in BMI/obesity. The marginal effect for BMI and obesity is 125.696 (40.306) and 2062.187
(732.5577) for IV estimation, and 68.848 (9.540) and 647.113 (116.860) for non-IV estimation
(standard errors in parentheses). These results are very comparable to C&M’s results. The
2
3. corresponding marginal effects that they report are 149 (35) and 2741 (745) for BMI/obesity IV
estimation, and 49 (9) and 656 (113) for BMI/obesity non-IV estimation.
Table 1: Point Estimates from Two-Part Model (2000 to 2005 data)
IV (total expenditure) Non-IV (total expenditure)
(1) (2) (3) (4) (5) (6) (7) (8)
VARIABLES logit glm logit glm logit glm logit glm
BMI 0.000 0.037*** 0.025*** 0.016***
(0.011) (0.013) (0.002) (0.003)
Obesity 0.136 0.600*** 0.240*** 0.152***
(0.190) (0.224) (0.030) (0.036)
Observations 40,472 40,472 40,472 40,472 40,232 40,232 40,232 40,232
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Table 2: Marginal Effect Estimates from Two-Part Model (2000 to 2005 data)
IV (total expenditure) Non-IV (total expenditure)
VARIABLES (1) (2) (3) (4)
BMI 125.696** 68.848***
(40.306) (9.540)
Obesity 2062.187* 647.113***
(732.5577) (116.860)
Observations 40,472 40,472 40,232 40,232
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
3.3 Limitations
C&M acknowledge that their model has several limitations. The first is associated with the nature of
the instrumental variables approach. There is no definite way to check the validity of the instrument.
The assumptions they make in justifying the use of BMI of the oldest child as an instrument are
the following: the weight of a biological family member is strongly correlated with the weight of a
respondent, but uncorrelated with the residual medical care costs. However, the authors acknowledge
that it is possible that the genes that influence weight may also affect other unknown factors that
could affect residual medical care costs.
The second limitation to this model is a data problem. The data on BMI and medical care costs is
available only for a single interval of time for all observations. Access to longitudinal data would
3
4. increase our understanding of the long-term effects of obesity on medical costs. Other limitations
include possible mismeasurement in the data and the generalizability of the limited sample, namely,
in this case due to the nature of the instrument, we restrict our sample to adults with biological
children. The true effect of obesity in the population may in fact be an underestimate due to the
positive health spillovers resulting from family responsibility.
4 Modifications
Given our successful replication, we proceed to propose several modifications to the model. We
decide that C&M’s empirical two-part model is the correct way of conducting estimation for our
data as opposed to other models that also address the problem of having a large proportion of zero
values in our data. The reason why we agree with C&M about the validity of the two-part model
is that health expenditures are actual outcomes (i.e. true zeros) because in such cases no money
for health care is expended. This is in contrast to potential outcomes, where the zeros we observe
in the data are “missing values”. The classic example of this is with non-working women. Their
observed wages are zero, but this is only because they choose not to select into the workforce. If
they did, they would earn a positive wage that we do not observe (i.e. the zeros we observe are not
“true zeros”). Therefore, various papers like Frondel and Vance (2012) have argued that in the case
of true zeros, a two-part model is more appropriate.
The first basic modification we implement is to expand the time horizon we analyze since we have a
larger dataset. We include data from 2000 to 2012. We find that the marginal effect of obesity on
medical expenditure has increased relative the smaller time horizon. Our results are summarized in
Table 3. This model constitutes our baseline estimate of the marginal effect of obesity on healthcare
costs.
Table 3: Marginal Effect Estimates from Two-Part Model (2000 to 2012 data)
IV (total expenditure) Non-IV (total expenditure)
VARIABLES (1) (2) (3) (4)
BMI 169.425*** 85.616***
(30.313) (7.234)
Obesity 3297.591*** 982.399***
(558.406) (97.053)
Observations 88,880 88,880 88,880 88,880
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
4
5. 4.1 Further Controls
One improvement that we make is to add two control variables to account for the general health
status and insurance coverage of each individual. As previously mentioned, the existing baseline
model inspired by C&M does not take into account other possible genetic factors that may affect
residual health expenditure through unknown genetic mechanisms not directly related to obesity.
Specifically, although it is difficult to test given the expertise required in biology, genes that affect
weight might affect other health-related factors that then affect medical expenditure. In order to
examine if these unknown factors influence the results from the baseline model of the paper, we
include two more control variables into our model. One measures respondents’ perception of his or
her own health, and the other indicates the status of respondents’ insurance coverage. We definitely
want to filter out the incremental health expense caused by diseases that are not derived or related
to obesity but still have genetic causes. Health perception allows us to take advantage of one’s
self-knowledge of his/her health to control for such non-obesity related diseases. If one thinks he/she
is less healthy, it would be more likely that the person is suffering from other diseases that are not
related to obesity, which may incur a medical expense that should be controlled for. But using
only health perception does not completely filter out the effect of non-obesity related diseases since
obese people will tend to consider themselves unhealthy. Insurance coverage could play a role by
additionally controlling for the non-obesity related diseases that are unaccounted for by health
perception. Insurance companies will be more likely to reject advanced insurance coverage to obese
people than people with latent diseases with genetic causes. Hence, together health perception and
insurance coverage could help sharpen the precision of our estimation. Our results are summarized
in Table 4.
Table 4: Marginal Effect Estimates from Two-Part Model with Further Controls (2000 to 2012 data)
IV (total expenditure) Non-IV (total expenditure)
VARIABLES (1) (2) (3) (4)
BMI 93.136*** 35.979***
(29.527) (6.676)
Obesity 1956.029*** 293.408***
(552.047) (90.678)
Observations 88,880 88,880 88,880 88,880
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
As seen from the significant decrease in the marginal effect of BMI/obesity on healthcare expenditure
after including additional controls, we believe that the control variables that we incorporated are
valid.
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6. 4.2 Investigating Time Heterogeneity
Additionally, we observe that our estimates of the marginal effects of obesity when we expand the
sample from the 2000-2005 time period to the 2000-2012 time period increase quite a bit (before
we implement further controls). This difference is seen by comparing Table 3 to Table 2, as these
two regressions contain the same controls and differ only in sample size. Specifically, a one-unit
increase in BMI causes a $169 increase in medical expenditure compared to $125 in the smaller
sample, and “becoming obese” as measured by the obesity indicator causes a $3,297 increase in
medical expenditure compared to $2,062 in the smaller sample. Thus, we proceed by investigating
whether there exists time heterogeneity. In other words, we investigate whether the marginal effect
of BMI/obesity on medical expenditure, accounting for all our controls including the ones above,
has changed over time.
We implement this investigation by interacting the year fixed effects with the instrumented BMI/obe-
sity variable for both parts of the two-part model. We leave out the dummy for year 2000 to avoid
multicollinearity. We then examine the marginal effects of the interaction terms. We discover that
none of the interaction terms show significant marginal effects across both measures of obesity,
except for the year 2012. According to Frondel and Vance (2012), the significance of the interaction
term is sufficient to establish that there is heterogeneity. However, the actual magnitude and sign of
the effect is hard to determine. Obamacare could be the one potential source of this heterogeneity
given that it was implemented in 2012. We can draw no such inferences from our findings thus far,
and the causes of such time heterogeneity may be a fruitful topic for further research.
4.3 Final Model Results and Discussion
Following these investigations, our final model incorporates two control variables with C&M’s
instrumented two-part model. These results are presented in Table 4.
Our results conclude that a one-unit increase in BMI causes a $93 increase in medical expenditure,
and being obese causes a $1,956 increase in medical expenditure. They suggest that it is possible
that the results from C&M are overestimated. C&M acknowledge that their estimates are higher
than previous research has shown. Our modifications result in an intermediate value between C&M’s
paper and previous literature.
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7. 5 Conclusion
This report attempts to improve the econometric model suggested by C&M to better measure the
causal impact of obesity on medical costs. Their model adopts an instrumental variables approach
that uses BMI level and obesity status of the oldest child in each household as an instrument for
each respondent’s corresponding BMI/obesity level. It uses a two-part model involving a logit and
gamma GLM with a log link to estimate the marginal effects of BMI level and obesity on health
expenditure. Although they argue that their choice of instrument satisfies validity and exogeneity,
we believe that their model does not take into account the possibility that the genes that affect
weight affect other health-related factors. In order to address this issue, we add new control variables
that reflect the health status and insurance coverage of each respondent. We conclude that the
marginal effect of obesity on medical expenditure decreases from $3,297 to $1,956 after the inclusion
of these variables.
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8. 6 References
Cawley, John and Meyerhoefer, Chad. “The medical care costs of obesity: An instrumental
variables approach.” Journal of Health Economics, 2012, 31(1), pp.219-230.
Frondel, Manuel and Vance, Colin. “On Interaction Effects: The Case of Heckit and Two-Part
Models.” Ruhr Economic Papers, 2012, 309(1), pp.1-21.
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