Unbiased Analysis of Today's Healthcare Issues
Yesterday it was announced that Claudia Goldin won the 2023 Nobel Prize in Economics (formerly the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel). She is the 3rd woman to have won the economics Nobel. The Nobel press release summarizes her research as follows: Women are vastly underrepresented in the global labour…
That is the title of an interesting review paper by Squires et al. 2023. The abstract is below: The effectiveness and cost of a public health intervention is dependent on complex human behaviors, yet health economic models typically make simplified assumptions about behavior, based on little theory or evidence. This paper reviews existing methods across…
Health care data–particularly spending data–often has a right skewed distribution with a high number of 0’s. For instance, US health care spending in 2019 was $11,852. However, many people don’t get sick and have no health care spending. Moreover, people generally don’t have negative health care spending. Further, some many patients with serious diseases rack…
Let’s say that you have data. This could be say data on how many points Giannis Antetokounmpo scored in a game. One could fit some distribution to the data. For instance, you may know the mean and standard deviation and you could estimate a normal distribution. However, the data may not be normal. Or the…
What is the cost of an intervention to improve health? This seems like a simple question, just add up the costs. However, some costs are variable and some are fixed. Thus, adding an extra person to an intervention may not equal to the average cost as fixed costs may not increase. Additionally, there may be…
What is the impact of being insured on health outcomes? This is a difficult question to answer in part because of adverse selection (e.g., sicker patients may choose to be insured). But even absent adverse selection, the ability to diagnosis a disease may vary between the insurer and uninsured. Consider this example from Kaliski (2023):…
An interesting recent paper by Moler-Zapata, Grieve, Basu, and O’Neill (2023) compares local instrumental variables (LIV) with two-stage least squares (2SLS) to IV. Local instrumental variable (LIV) approaches use continuous/multi-valued instrumental variables (IV) to generate consistent estimates of average treatment effects (ATEs) and Conditional Average Treatment Effects (CATEs). There is little evidence on how LIV…
Let’s say you are interested in measuring the relationship between type 2 diabetes mellitus (T2DM) and depression. In many cases, one would use electronic health records data and conduct a logistic regression with depression as the dependent variable and T2DM (potentially along with demographics and other comorbidities) as the independent variables. However, the use of…
That is the title of an article by Cascaldi-Garcia et al. (2023) in the Journal of Economic Literature. I summarize some of the key findings below. Definitions: Risk: Applies to situations in which the outcome is unknown to decision makers, but the probability distribution governing the outcome is known” Volatility. Often used synonymously with risk,…
Summarized from a nice review paper by Basu, Unuigbe and Masseria (2023). Stratifying by Quintiles of propensity scores (PS). Under this approach, the empirical distribution of the estimated PS across the entire sample (including treated and untreated participants) is divided into quintiles. Indicator variables for the first 4 quintiles are then used as covariates, along…