By JEFF GOLDSMITH

In late June, 2024, two economists, Zarek Brot-Goldberg and Zack Cooper, from the University of Chicago and Yale respectively, released an economic analysis arguing that hospital mergers damage local economies and result in an increase in deaths by suicide and drug overdoses in the markets where mergers occur. Funded by Arnold Ventures  their study characterizes these mergers as “rent seeking activities” by hospitals seeking to use their economic power to extort financial gains from their communities without providing any value. 

The Brot-Goldberg-Cooper analysis was a spin-off of a larger study decrying the lack of federal anti-trust enforcement regarding hospital mergers. These two studies used the same economic model. The data were derived from the Healthcare Cost Institute, a repository of commercial insurance claims information from three of the four largest commercial health insurers, United Healthcare, Humana and Aetna (a subsidiary of struggling pharmacy giant CVS) plus Blue Cross/Blue Shield. HCCI’s contributors account for 28% of the commercial health insurance market.

The authors use a complex econometric model to manipulate a huge, multifactorial data base comprising hospital merger activity, employer health benefits data, county level employment data and morbidity and mortality statistics. This data model enabled a raft of regression analyses attempting to ferret out “associations” between the various domains of these data.

Using HCCI’s data, the authors construct what they termed a  “causal chain” leading from hospital mergers to community damage during their study period–2010 to 2015.  It looked like this: hospital mergers raise prices for private insurers-these prices are passed on to employers–who respond by laying off workers–some of whom end up killing themselves. So, according to the logic, hospital mergers kill people. Using the same methodology, the authors argued that between 2007 and 2014, hospital price increases of all sorts killed ten thousand people. 

A classic problem with correlational studies of this kind is their failure to clarify the direction of causality of data elements.  The model lacked a control group–comparable communities that did not experience hospital mergers during this period–because the authors argued that mergers were so pervasive they could not locate comparable communities that did not experience them.    

The model focused on a subset of 304 hospital mergers from 2010 to 2015, culled from a universe of 484 mergers nationally during the same period. The authors excluded mergers of hospitals that were further than fifty miles apart, as well as hospitals with low census. The effect of these assumptions was to exclude most rural hospitals and concentrate the mergers studied in metropolitan areas and cities. The densest cluster was in the I-95 corridor between Washington DC and Boston. See the map below:

According to the model, these mergers resulted in an average increase of 1.2% in hospital prices to commercial insurers, 91% of which were passed to their employer customers in those markets. This minuscule rate increase had a curiously focused and outsized effect–a $10,584 increase in the median employer’s health spending in the merged hospitals’ market.

According to the model, local employers “responded” to this cost increase by reducing their payrolls by a median amount of $17,900, all through layoffs–70% more than the alleged merger cost increase. This large overage was not explained by the authors. Moreover, the layoffs took place almost immediately, in the same year as the merger-induced increases, even though many health insurance contracts are multi-year affairs, and lock hospitals in to rates for that period.

At the end of the “causal chain,” 1 in 140 laid off people in those communities for whatever reason killed themselves through suicide or drug overdoses. By extrapolation, the authors accuse the perpetrators of overall hospital rate increases of killing ten thousand people in the affected communities during seven years overlapping the study period.   

Failing the Test of Real World Plausibility

It is difficult to know where to begin to unravel this complex web of “associations”. The biggest puzzle is the magnification of impact of a 1.2% “shock”, as the authors term it, to health benefits cost on employers. To put this in in perspective, the 1.2% average price increase is against a health benefit that amounts to about 9%, on average,  of the sample employers’  total compensation costs.* So, 1.2% increase of a 9% cost is an exactly one tenth of 1% (.001) increase in employer’s total compensation expense. Payroll and benefits are in turn perhaps 50-70% of an employer’s total operating expense. A one-tenth of a percent increase in employment costs is a mosquito bite, not a “shock”.

The authors’ inferred leap from this micro-increase in employer costs to widespread layoffs is indefensible compared to real world managerial behavior. The authors ruled out the most obvious cost response–cost-shifting to workers by raising their deductibles or copayments–on the grounds that they found no changes in Health Savings Account enrollment in the sample during the five year study period. After more than quadrupling from 2006 to 2011, growth in HSA enrollment levelled off during the last four years of the study period. 

Yet employer cost shifting to workers by raising their out-of-pocket spending limits accelerated during the same period. According to Kaiser/HRET’s annual survey, from 2010 to 2015, the number of workers with deductibles and co-insurance more than $2,000 doubled. Thus, the effects of any commercial  health insurance rate increases, whatever their size, were likely shared broadly across the entire covered population in the merger-affected markets.

Most employers facing economic challenges bend over backwards to avoid parting company with productive, experienced workers. And they have a wide range of options to avoid doing so: raising prices, cutting hourly workers’ hours, shifting workers from employees to contractors (dodging benefits expenses altogether), pressuring other suppliers for discounts, improving productivity, finding new customers, and “shrinkflation” in their product (e.g. a half-ounce smaller Almond Joy bar for the same price ). None of these responses were measured or controlled for in the model. 

Meager Exercise of Market Power

If an anti-competitive exercise of market power was the goal of the mergers, then an average 1.2% rate increase struck us as a remarkably meager exercise of that power. If merging hospitals were free to charge what they wanted because they had increased bargaining power, why not charge 10% or 20% more after merging? According to colleagues who work in hospital mergers and acquisitions, transaction costs in a merger can run 3-5% of an acquired hospitals’ annual revenues, and the complex transaction itself is a huge hassle.   

A 1.2% post-merger rate increase would not even cover the transaction costs of the merger (legal, accounting and actuarial analysis, deal brokers and investment banker fees, systems integration consultants to get their different “instances” of EPIC to communicate and outside experts to assist in operational improvements, etc), let alone yield any actual cash flow to the merged entity.    

What Caused the Deaths?

As for the final leap -that one in 140 of the workers presumably laid off by these employer reductions in force kills themselves or overdose on drugs–authors made no traceable efforts to tie these deaths to the companies allegedly laying off workers. Here, in particular, the absence of control groups makes it impossible to determine how much contribution, if any, hospital rate increases or mergers made to the overall upward trend of deaths of despair in the affected communities, as opposed to other similar communities.

The authors did not control for the other factors that lead to deaths of despair in the target communities and that might introduce “covariance” error–divorces, deaths of loved ones, broad-based declines in employment opportunities, closures of churches, schools, or community based services, declining availability of mental health services or the sudden arrival of deadly fentanyl in the local market. 

Remember the authors deliberately oversampled urban markets.  Many of the urban counties covered in the study (Cook County-Chicago, Nassau County-Queens, Wayne County-Detroit, Philadelphia County e.gs) are both vast and rife with urban poverty. To tie deaths of despair in those troubled communities to hospital mergers is an egregious case of victim-blaming. The same forces that lead to deaths of despair–the loss of economic opportunity and social support–are the ones harming their hospitals and leading to their mergers or closure. 

What Caused the Mergers in the First Place?

I am an expert in hospital strategy. In 1980, I wrote a book called Can Hospitals Survive, which predicted much of the ensuing consolidation in the industry. In my forty year consulting career, I worked for more than a dozen struggling hospitals that wanted to retain their independence. In my long industry experience, hospital boards and medical staffs do not willingly surrender their autonomy and historical identity unless forced to by economic circumstances.

The reality is most hospital mergers aren’t really mergers at all, they are acquisitions of a poorly performing independent hospital or system by a larger hospital or  health system. No less an authority on hospital financing than MedPac Chair Michael Chernew found that higher levels of Medicare funding as a percentage of total predispose hospitals to failure and, subsequently, merger. An excessive concentration of Medicaid and uninsured patients has an even more adverse impact. Cost shifting to employers, and charging those employers more is the only way economically challenged hospitals can recover their losses. The authors did not measure or assess the role that adverse payor-mix changes played in hospital rate increases, or analyze its role in causing hospitals to merge. They attempted, unsuccessfully in my opinion, to adjust for failing local economies as a cause of mergers, employer layoffs and deaths of despair by excluding low census hospitals from their merger analysis as well as excluding most rural hospital mergers. But many hospitals that are failing are full of publicly funded patients whose coverage pays 85% or less of their actual costs, or of patients with no insurance at all. 

Merger or Closure:  The Real Choice Facing Communities

Most importantly in my view, the authors made no effort to compare the effects of a merger to that of the loss of the hospital itself. Hospitals are often the community’s largest employer. The authors estimate that merger-induced rate increases raise the unemployment rate in the local market around the merged hospital by perhaps a tenth of a percentage point. 

By contrast, hospital closure in a rural community can raise the unemployment rate by 1.6 percentage points–that is a sixteen-fold greater effect. Measuring the unemployment effect in small or large metropolitan labor markets is more challenging but the effects of hospital closure can run into the many hundreds of jobs lost, not counting the “multiplier” effects on suppliers, industry partners and neighboring businesses.   

Whether saving these hospitals was worth the price paid by a post-merger rate increase is the real-world policy question. Though this proposition needs empirical testing, I believe that most communities would willingly trade a 1.2% health insurance rate increase for avoiding the loss of their hospital. Excluding the low census hospitals from their regression runs was the authors’ failed attempt to avoid discussing this tradeoff. 

Regardless of the condition of the mergee, the authors made a whopping value judgment in assuming that that the 1.2% average post-merger rate increase was simply extortion, borne of increased market leverage, not the price paid for continuing that hospital’s operations in the community. The authors also made no effort to measure post-merger service enhancements, such as increased physician retention or recruitment, management or capital improvements or other tangible benefits.

Though some studies the authors cite fail to find cost reduction or quality benefits accruing from hospital mergers, more recent analyses (for roughly the same study period as Cooper’s) found convincing evidence that a stronger operating partner results in a statistically significant drop in costs (-2.5%) per adjusted admission at acquired hospitals, which is consistent with the hospitals gaining economies of scale post-merger. There was also statistically significant decline (-3.9%) in revenue per adjusted admission, which casts doubt upon the ‘mergers lead to higher prices’ hypothesis. 

Adequacy of Hospital Capacity a Pressing Concern

During the 1970’s, federal health policy in United States focused on controlling the supply of  hospitals and hospital beds in the US, believing that efforts to fill empty beds were a major driver of health cost growth, then in double digits. At the time of Can Hospitals Survive, 30% of hospitals were already part of systems. Since that time, nearly a thousand hospitals have closed, and the proportion of hospitals that are part of multi-hospital systems has grown to 70% or better. Hospital inpatient utilization measured by hospital days per thousand has fallen by half since 1980.

Compared to the Health Planning statute’s target of 4 acute care beds per thousand, today the US is at 2.3 beds per thousand. The US now has the 26th lowest ratio of hospital beds to population of the 28 OECD countries.  During the pandemic, many communities experienced critical shortages of hospital ICU beds, ER capacity, etc. and were housing patients requiring intensive medical services in tents in the parking lot.

Faced with a choice between merging struggling hospitals into larger systems and losing those hospitals–both their service capacity and their employment–through hospital closure, the US does not have a margin of error. If the surviving multi-hospital health systems cannot demonstrate measurable benefits not only to acquired hospitals but also for the communities they serve, there are going to be serious repercussions for the future of Medicare and for the nation’s health.   

This leap from a one-tenth of one percent increase in employment costs to widespread layoffs and deaths in the affected markets calls into question not only authors’ methodology but also their objectivity. The most charitable explanation for this odd focus on layoffs is what behavioral economists call “confirmation bias,”  the unconscious tendency we all have to ignore data or relationships that do not confirm pre-existing expectations. 

Investigators succumbing to confirmation bias tend to find what they set out to find.

A less charitable explanation is that the authors found what their funders (e.g. Arnold Ventures) expected them to find, given the investigators’ past research findings and the funder’s desired policy conclusion, that anti-competitive conduct damages local economies and needs to be reined in.

In either case, a rigorous search for actual causes is circumvented, and readers are misled.

Nobel Laureate economics Ronald Coase once said:  “If you torture data long enough, it will confess to anything.” That maxim seemed fully relevant to the analysis of merger impacts we have been discussing. In my view as a long time participant/observer of the hospital consolidation process, the authors made no contribution whatever to solving the problem of how to assure access by struggling communities to hospital services, nor making those services more affordable to those communities’ citizens. Actually working in those communities is a useful antidote against being hypnotized by econometric models. Blaming hospital mergers for damaging their communities’ economies and killing its citizens is not only a gratuitous insult to the involved hospitals and their boards, it also moves us no closer to a sustainable health system going forward. 

The research underpinning this paper was funded by the Federation of American Hospitals, but the opinions expressed therein are the author’s alone. Trevor Goldsmith assisted with research on this paper, and Richard Bajner and Keith Pitts provided insights into the economics of hospital merger transactions. 

Jeff Goldsmith is a veteran health care futurist, President of Health Futures Inc and regular THCB Contributor. This comes from his personal substack