: Heather Brown
: The Economics of Public Health Evaluating Public Health Interventions
: Palgrave Pivot
: 9783319748269
: 1
: CHF 85.20
:
: Volkswirtschaft
: English
: 112
: Wasserzeichen/DRM
: PC/MAC/eReader/Tablet
: PDF

on-communicable diseases have surpassed infectious diseases as the leading cause of morbidity and mortality in developed countries. Prevention and treatment of the causes and consequences of lifestyle-related diseases forms an important part of health policy in the twenty-first century. Public health economics - from quantifying the problem, to evaluating interventions and developing toolkits to assist decision makers - is an essential area for any postgraduate student and researcher with an interest in applied economics to understand.

There are a wide range of techniques from mainstream economics and health economics that can be applied to the evaluation of public health policy and public health issues. In this book, Brown presents examples from developed countries to illustrate how economic tools can be applied to public health. Further, cross-country comparisons illustrate how contextual factors related to healthcare systems, demographics and environmental factors may impact on outcomes and the cost-effectiveness of public health policies, in order to aid understanding and help students apply theory into practice.  

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Heather Brown is Lecturer in Health Economics at Newcastle University, UK. She completed an MRC early career fellowship in the economics of health at the University of Aberdeen, UK. Her research interests include applied econometrics with a focus on understanding the relationship between health behaviours and outcomes and inequalities. She has published many peer reviewed publications on economics and public health.

Preface5
Contents7
List of Figures9
List of Tables11
Part I: Introduction12
1: Introduction to Public Health Economics13
Why Do We Need Economics in Public Health?13
What Makes Public Health Different from the Production of Televisions?16
What is Public Health Economics?19
The Real World21
References22
Additional Reading22
Part II: Data23
2: Observational Data24
The Rise of Big Data24
Cons of Panel Data31
Data Linkage31
References and Further Reading33
3: Missing Data and Sample Attrition34
Missing at Random or Missing at Non-Random34
Sample Attrition35
Our Example35
Sample Attrition38
Multiple Imputation39
Pros and Cons of MI vs IPW for Public Health Research44
References and Further Reading46
Part III: Policy Evaluation47
4: Correlations versus Causation48
Correlations48
Understanding Correlation Coefficients49
Strength of the Correlation49
Example49
Estimating Correlation Coefficients51
Correlation Analysis in Economic Evaluation of Public Health Policy53
Weaknesses of Correlation Analysis53
Causal Relationships54
How to Estimate a Causal Relationship54
Basic Econometric Tools for Estimating a Causal Relationship55
How Do You Know If You Have Found a Good Instrument?59
Interpreting IV Estimates60
References and Further Reading62
5: Before and After Study Designs63
Interrupted Time Series64
When to Use ITS64
Data Required to Estimate an ITS65
Estimating ITS66
Interpreting Results from ITS67
Regression Discontinuity Approach67
When to Use RD68
How to Use RD69
Implementation in Practice73
Generalisability of the Results74
Fuzzy RD Approach74
Steps to Estimation76
Difference in Difference Approach (DiD)78
Some Things to Keep in Mind81
Empirical Papers Using These Estimation Techniques84
References and Further Reading85
Interrupted Time Series85
Regression Discontinuity85
Difference in Difference86
6: Cross-Country Comparisons87
How to Conduct Cross-Country Analysis89
Identifying Data Sources89
Analysis Method90
The Example91
Propensity Score Matching in Cross-Country Analysis91
When to Use PSM92
Matching92
How to Implement in Practice95
Some Extensions96
Interpretation of Coefficients97
A Further Example98
Data99
Constructing Treated and Non-Treated Groups99
Other Methods for Estimating Cross-Country Differences103
References and Further Reading104
7: A Practitioner’s Guide106
Define Your Research Question106
Identify an Appropriate Dataset107
Estimate a Simple Regression Model108
Identify the Most Important Type of Bias that may be Impacting on your Simple Coefficient Estimates and Choose an Appropriate Model108
Compare Coefficients Between Chosen Model and Base Model109
References and Further Reading109
Index110