Category Archives: experimental design

A Medical Forecasting Controversy – Increased Deaths from Opting-out From Expanding Medicaid Coverage

Menzie Chinn at Econbrowser recently posted – Estimated Elevated Mortality Rates Associated with Medicaid Opt-Outs. This features projections from a study which suggests an additional 7000-17,000 persons will die annually, if 25 states opt out of Medicaid expansion associated with the Affordable Care Act (ACA). Thus, the Econbrowser chart with these extrapolations suggests within only few years the additional deaths in these 25 states would exceed causalities in the Vietnam War (58,220).

The controversy ran hot in the Comments.

Apart from the smoke and mirrors, though, I wanted to look into the underlying estimates to see whether they support such a clear connection between policy choices and human mortality.

I think what I found is that the sources behind the estimates do, in fact, support the idea that expanding Medicaid can lower mortality and, additionally, generally improve the health status of participating populations.

But at what cost – and it seems the commenters mostly left that issue alone – preferring to rant about the pernicious effects of regulation, implying more Medicaid would actually probably exert negative or no effects on mortality.

As an aside, the accursed “death panels” even came up, with a zinger by one commentator –

Ah yes, the old death panel canard. No doubt those death panels will be staffed by Nigerian born radical gay married Marxist Muslim atheists with fake birth certificates. Did I miss any of the idiotic tropes we hear on Fox News? Oh wait, I forgot…those death panels will meet in Benghazi. And after the death panels it will be on to fight the war against Christmas.

The Evidence

Econbrowser cites Opting Out Of Medicaid Expansion: The Health And Financial Impacts as the source of the impact numbers for 25 states opting out of expanded Medicaid.

This Health Affairs blog post draws on three statistical studies –

The Oregon Experiment — Effects of Medicaid on Clinical Outcomes

Mortality and Access to Care among Adults after State Medicaid Expansions

Health Insurance and Mortality in US Adults

I list these the most recent first. Two of them appear in the New England Journal of Medicine, a publication with a reputation for high standards. The third and historically oldest article appears in the American Journal of Public Health.

The Oregon Experiment is exquisite statistical research with a randomized sample and control group, but does not directly estimate mortality. Rather, it highlights the reductions in a variety of health problems from a limited expansion of Medicaid coverage for low-income adults through a lottery drawing in 2008.

Data collection included –

..detailed questionnaires on health care, health status, and insurance coverage; an inventory of medications; and performance of anthropometric and blood-pressure measurements. Dried blood spots were also obtained.

If you are considering doing a similar study, I recommend the Appendix to this research for methodological ideas. Regression, both OLS and logistic, was a major tool to compare the experimental and control groups.

The data look very clean to me. Consider, for example, these comparisons between the experimental and control groups.


Here are the basic results.


The bottom line is that the Oregon study found –

..that insurance led to increased access to and utilization of health care, substantial improvements in mental health, and reductions in financial strain, but we did not observe reductions in measured blood-pressure, cholesterol, or glycated hemoglobin levels.

The second study, published in 2012, considered mortality impacts of expanding Medicare in Arizona, Maine, and New York. New Hampshire, Pennsylvania, and Nevada and New Mexico were used as controls, in a study that encompassed five years before and after expansion of Medicaid programs.

Here are the basic results of this research.


As another useful Appendix documents, the mortality estimates of this study are based on a regression analysis incorporating county-by-county data from the study states.

There are some key facts associated with some of the tables displayed which are in the source links. Also, you would do well to click on these tables to enlarge them for reading.

The third study, by authors associated with the Harvard Medical School, had the following Abstract

Objectives. A 1993 study found a 25% higher risk of death among uninsured compared with privately insured adults. We analyzed the relationship between uninsurance and death with more recent data.

Methods. We conducted a survival analysis with data from the Third National Health and Nutrition Examination Survey. We analyzed participants aged 17 to 64 years to determine whether uninsurance at the time of interview predicted death.

Results. Among all participants, 3.1% (95% confidence interval [CI] = 2.5%, 3.7%) died. The hazard ratio for mortality among the uninsured compared with the insured, with adjustment for age and gender only, was 1.80 (95% CI = 1.44, 2.26). After additional adjustment for race/ethnicity, income, education, self- and physician-rated health status, body mass index, leisure exercise, smoking, and regular alcohol use, the uninsured were more likely to die (hazard ratio = 1.40; 95% CI = 1.06, 1.84) than those with insurance.

Conclusions. Uninsurance is associated with mortality. The strength of that association appears similar to that from a study that evaluated data from the mid-1980s, despite changes in medical therapeutics and the demography of the uninsured since that time.

Some Thoughts

Statistical information and studies are good for informing judgment. And on this basis, I would say the conclusion that health insurance increases life expectancy and reduces the incidence of some complaints is sound.

On the other hand, whether one can just go ahead and predict the deaths from a blanket adoption of an expansion of Medicaid seems like a stretch – particularly if one is going to present, as the Econbrowser post does, a linear projection over several years. Presumably, there are covariates which might change in these years, so why should it be straight-line? OK, maybe the upper and lower bounds are there to deal with this problem. But what are the covariates?

Forecasting in the medical and health fields has come of age, as I hope to show in several upcoming posts.