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Opting Out Of Medicaid Expansion: The Health And Financial Impacts

January 30th, 2014

The Affordable Care Act (ACA) was designed to increase access to health insurance by: 1) requiring states to expand Medicaid eligibility to people with incomes less than 138 percent of the Federal Poverty Level (FPL) ($19,530 for a family of three in 2013), with the cost of expanded eligibility mostly paid by the federal government; 2) establishing online insurance “exchanges” with regulated benefit structures where people can comparison shop for insurance plans; and 3) requiring most uninsured people with incomes above 138 percent FPL to purchase insurance or face financial penalties, while providing premium subsidies for those up to 400 percent of FPL.

Recent studies suggest that Medicaid expansion will result in health and financial gains.  Older studies also found salutary health effects of expanded or improved insurance coverage, particularly for lower income adults. These studies also document an increase in utilization of most health care services. Most recently, the Oregon Health Insurance Experiment (OHIE) found a striking increase in emergency department use as well as other outpatient care.

The Supreme Court ruled in June 2012 that states may opt out of Medicaid expansion, and as of November 2013, 25 states have done so. These opt-out decisions will leave millions uninsured who would have otherwise been covered by Medicaid, but the health and financial impacts have not been quantified.

In this post, we estimate the number and demographic characteristics of people likely to remain uninsured as a result of states’ opting out of Medicaid expansion. Applying these figures to estimates of the effects of insurance expansion from prior studies, we calculate the likely health and financial impacts of states’ opt-out decisions.

The Consequences of Opting Out

The Supreme Court’s decision to allow states to opt out of Medicaid expansion will have adverse health and financial consequences. Based on recent data from the Oregon Health Insurance Experiment, we predict that many low-income women will forego recommended breast and cervical cancer screening; diabetics will forego medications, and all low-income adults will face a greater likelihood of depression, catastrophic medical expenses, and death. Disparities in access to care based on state of residence will increase. Because the federal government will pay 100 percent of increased costs associated with Medicaid expansion for the first three years (and 90 percent thereafter), opt-out states are also turning down billions of dollars of potential revenue, which might strengthen their local economy.

The ACA’s tax subsidy for insurance purchase on the Exchanges is only available to persons with incomes above 100 percent of FPL.  People below this threshold in opt-out states (the so-called low-income “coverage gap”) will see no benefit as the law goes into effect.  They may even see harm because the ACA cuts disproportionate share (DSH) funding to safety net hospitals, reducing the resources available to care for the remaining uninsured.

Despite the widely held belief that almost all Americans will be insured under the ACA, more than 32 million people will remain uninsured after the law goes into effect. Even in states that opt in to Medicaid expansion, millions will remain without coverage.

Low-income adults in states that have opted out of Medicaid expansion will forego gains in access to care, financial well-being, physical and mental health, and longevity that would be expected with expanded Medicaid coverage.

Examining the numbers. The number of uninsured people in states opting in and opting out of Medicaid expansion is displayed in Exhibit 1. Nationwide, 47,950,687 people were uninsured in 2012; the number of uninsured is expected to decrease by about 16 million after implementation of the ACA, leaving 32,202,633 uninsured.  Nearly 8 million of these remaining uninsured would have gotten coverage had their state opted in.  States opting in to Medicaid expansion will experience a decrease of 48.9 percent in their uninsured population versus an 18.1 percent decrease in opt-out states.

Exhibit 1: Uninsured Population by State, Pre- and Post-ACA 

Dickman-Exhibit 1

Predicted national-level consequences of states opting out of Medicaid expansion are displayed in Exhibit 2. We estimate the number of deaths attributable to the lack of Medicaid expansion in opt-out states at between 7,115 and 17,104.  Medicaid expansion in opt-out states would have resulted in 712,037 fewer persons screening positive for depression and 240,700 fewer individuals suffering catastrophic medical expenditures. Medicaid expansion in these states would have resulted in 422,553 more diabetics receiving medication for their illness, 195,492 more mammograms among women age 50-64 years and 443,677 more pap smears among women age 21-64. Expansion would have resulted in an additional 658,888 women in need of mammograms gaining insurance, as well as 3.1 million women who should receive regular pap smears.

Exhibit 2: Effects of Medicaid Expansion on Health and Financial Outcomes, and National Estimates of Adverse Outcomes Avoided or Appropriate Screening/ Treatment Provided If Current Opt-out States Accepted Medicaid Expansion (for an enlarged view, click on the chart below)

Dickman-Exhibit 2 caption

State-level estimates for post-ACA effects of opting out of Medicaid expansion are displayed in Exhibit 3. In Texas, the largest state opting out of Medicaid expansion, 2,013,025 people who would otherwise have been insured will remain uninsured due to the opt-out decision. We estimate that Medicaid expansion in that state would have resulted in 184,192 fewer depression diagnoses, 62,610 fewer individuals suffering catastrophic medical expenditures, and between 1,840 and 3,035 fewer deaths.

Exhibit 3: State Estimates of Adverse Outcomes Avoided or Appropriate Screening /Treatment Provided If Current Opt-out States Accepted Medicaid Expansion (for an enlarged view, click on the chart below)

Dickman-Exhibit 3 copy


We categorized states as opting in or opting out of Medicaid expansion using the Kaiser Family Foundation’s “Status of State Action on the Medicaid Expansion Decision,” which was updated on November 22, 2013. We used the Census Bureau’s 2013 Current Population Survey, a nationally representative survey of the non-institutionalized US population, to determine the number of uninsured people in each state before implementation of the ACA. We then projected the number of uninsured people in each state after implementation of the ACA depending on whether the state is opting in or opting out of Medicaid expansion. Based on previously published estimates of take-up rates and estimates from the Congressional Budget Office, we assumed that in states opting out, 90 percent of currently uninsured people with incomes below 138 percent of FPL will remain uninsured, as will 75 percent of uninsured people with incomes above 138 percent FPL. In states opting in, we assume that 40 percent of currently uninsured people with incomes below 138 percent FPL will remain uninsured, as will 60 percent of uninsured people with incomes above 138 percent FPL. These estimates incorporate the assumption that enrollment of people with incomes above 138 percent FPL through the exchanges will be higher in states that opt to expand Medicaid.

We used data from three sources to estimate the effects of Medicaid expansion: The Oregon Health Insurance Experiment; and two widely cited estimates of the impact of coverage expansion on mortality. The OHIE is a randomized study that examined the effects of expanding public health insurance for low-income (less than 100 percent FPL) adults on health, financial strain, health care use, and self-reported well-being.  It found that after an average of 17 months of exposure to Medicaid coverage, improvements occurred in rates of depression (based on the eight-question version of the Patient Health Questionnaire (PHQ-8)), and catastrophic medical expenditures. In addition, the OHIE found that acquisition of coverage led to increased utilization of most types of health care, including several types of care that has been linked to improved outcomes such as diabetics receiving medication to treat their diabetes and clinically indicated mammograms and cervical pap smears (in the past 12 months). An estimate of the number needed to insure was calculated by dividing the number of newly insured persons by the number of outcomes achieved.

To estimate the effect of Medicaid expansion on catastrophic medical expenditures (i.e. medical expenditures greater than 30 percent of annual income), we used the observed effect size from OHIE for adults up to 100 percent FPL.  In order to extrapolate this financial impact finding from the OHIE to near-poor and middle income persons, we assumed that the effect size of Medicaid expansion among adults between 100 percent and 138 percent FPL would be only half as large, and among adults between 138 percent and 400 percent FPL, only one quarter as large as the effect size observed in the OHIE.  To estimate the number of women eligible for cervical cancer screening and mammography, we used the age ranges for screening suggested by national consensus guidelines (21 to 64 years for pap smears and 50 to 64 years for mammograms), and applied the increase in pap smear and mammogram rates observed in the OHIE.

We estimated the range of likely mortality effects of Medicaid expansion. For our high estimate, we used the recent study by Sommers and colleagues that compared trends in mortality rates in states with Medicaid expansions (New York, Maine, and Arizona) to trends in states without such expansions. The Medicaid expansions were associated with a 6.1 percent decrease in mortality, or 19.6 deaths per 100,000 non-elderly adults. We conservatively used this population-based estimate, rather than their number-needed-to-insure figure of 176, because, as Sommer et al. pointed out, the latter figure reflects the fact that in their study, Medicaid preferentially enrolled sicker than average adults. For our low estimate, we used a study based on mortality follow-up of participants in the National Health and Nutrition Examination Study, which found a 40 percent increase in death rates among the uninsured, an effect size approximately 42 percent that found by Sommers.


Several caveats apply to our findings. Our figures, which use the number of uninsured in 2012 as the baseline, differ slightly from Congressional Budget Office figures based on projections of the numbers who would have been uninsured in several future years had the ACA not been passed. We could not take into account several factors that might influence the impact of Medicaid expansion. For instance, both the OHIE and Sommers estimates are based on Medicaid expansions that paid doctors pre-ACA reimbursement rates. Since the ACA will provide a two-year increase in Medicaid rates for primary care services, it is possible that access to care will improve more than was observed in those studies if more providers start accepting Medicaid. In addition, Oregon’s health costs (and presumably its rates of catastrophic medical expenditures) are slightly lower than national average.

The patients studied in the OHIE were slightly older than the uninsured poor in opt-out states, and more often female.  While we were able to adjust for these demographic differences in estimating cancer screening rates, it was not possible to do so for other effects. Similarly, we did not attempt adjustment for regional differences in depression prevalence, in the uninsured population, although such differences are probably small. If anything, the adjusted prevalence of major depression in Oregon appears slightly below the national average. An older sample population in the OHIE may have resulted in greater improvements in health and screening following Medicaid expansion, leading to a slight overestimate of effects in states with a younger uninsured population, whereas the female predominance in the OHIE may have resulted in a slight underestimate of effects in other states because males are more likely to have diabetes and other chronic conditions. In the OHIE, a relatively small number of persons were covered by the Medicaid expansion. The broader expansion under the ACA may put greater strain on the limited capacity of providers who accept Medicaid patients, curtailing utilization. Finally, participants in the OHIE had been uninsured for at least six months, and were concentrated in the Portland area. Impacts elsewhere might differ.

We used data from the Sommers and Wilper studies to calculate mortality impacts because the OHIE was underpowered to detect changes in death rates.  Although small improvements in hypertension prevalence (-1.3 percent) and Framingham risk score (-0.2 points) were observed in the OHIE, these did not achieve statistical significance.

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17 Responses to “Opting Out Of Medicaid Expansion: The Health And Financial Impacts”

  1. Bruce Thompson Says:

    I just came across this very interesting post, leading to a couple of questions:
    1. Are the estimates for Ohio correct? It is listed as an opt-in state, but has a reduction in uninsured (18%) typical of opt-out states.
    2. While Wisconsin is listed as an opt-out state, the situation is a bit more complex. The existing Medicaid program has been opened to single people and others not previously eligible. To make room for them, former recipients making over 100% of poverty were moved out and expected to buy insurance through the exchanges. Is there some way to adjust the estimates for this difference? I would expect the decline in uninsured would fall somewhere in the middle.

  2. John Grima Says:

    Why didn’t the authors project a finding for ‘delayed entry to care’? Is there a nuance with that outcome (from the September 2013 NEJM article) that makes it problematic?

  3. Randy LaGue Says:

    Your map has Vermont as one of the states that opted out , yet your exhibit #1 Table shows Vermont as one of the states that opted in ? I live in Vermont and know that Vermont is an opted in state Please change your Map Graphic to reflect the TRUTH. Thanks

  4. Christopher James Conover Says:

    No one doubts Medicaid (actual value=99%) is better for family finances than employer-based health coverage (actual value~80%). But at an added cost north of $5,000 per person, Medicaid has to do more than merely reduce out-of-pocket spending to justify its costs.

    The AJPH finding of a 40% increase in mortality risk associated with being uninsured is by far the highest in the literature that has directly tried to calculate this value. The claim that it likely understates the potential mortality gains from Medicaid expansion lacks credibility, especially since that gain is based on a comparison of the uninsured with those on private insurance. Dickman et al. provide no empirical support whatsoever for believing that the HEALTH benefits (as opposed to financial benefits) of Medicaid are remotely comparable to private coverage; the available evidence suggest this is not true.

    It’s difficult to believe that the differences in physical health measurement between the Kronick study and AJPH study would account for a 40 percent mortality hazard disappearing. The Kronick study both was substantially larger in size–permitting much more precise measurement of effects–and controlled for other characteristics that the Wilper team did not. Mr. Dickman’s dismissal of the methodological limitations of Kronick would be perhaps more credible if he’d alerted the reader to these in discussing the limitations of his own analysis and let interested readers decide this for themselves.

    Mr. Dickman is correct that the reported mortality increase in Maine in the Sommers et al. study is not statistically significant. My apologies for reporting otherwise. Table 2 in Sommers shows that the mortality change observed in Maine was statistically signficant from New York’s, but that does not entirely rule out the possibility of a net mortality gain in Maine. Nevertheless, it underscores my central point. The Sommers study examined 3 states and Medicaid was found to have a statistically significant effect in only one. Yet the Dickman team essentially extrapolated that mortality benefit to EVERY state even though we have no reason to believe that most states would be similar to NY than to ME or AZ. Take Maine as a case in point. The Dickman methodology concludes that 31 lives would be saved in Maine from Medicaid expansion even though the very study they use to make that extrapolation specifically found that Maine’s Medicaid program had no statistically significant effect on mortality. Given that the Sommers team cautioned specifically against precisely this sort of extrapolation, it’s pretty inappropriate to quote their conclusion without alerting readers to this caveat.

    I stand by my belief that the authors have grossly overestimated potential mortality gains associated with Medicaid expansion.

  5. Sam Dickman Says:

    Michael Bertaut brings up valid concerns about needed financing increases to the Medicaid program, though this was not the focus of our post. We did note that a broad Medicaid expansion may indeed strain the limited number of providers accepting Medicaid patients, a problem perpetuated by low reimbursement rates. We also noted that state spending will indeed increase if Medicaid is expanded. A recent study by the Kaiser Family Foundation finds that overall state expenditures will be 2.9 percent greater between 2013 and 2022 compared to if all states opted out, whereas federal expenditures will be 26 percent greater.

    One should also note that states opting for the Medicaid expansion receive a large influx of federal funds that would reduce hospitals’ uncompensated care and serve as an economic stimulus.

    Professor Conover questions the generalizability of the Wilper AJPH study to Medicaid patients. He only cites aspects of Medicaid coverage that are inferior to private insurance. In fact, Medicaid has markedly lower co-payments than most private plans, and no deductibles – critical features for the poor. Moreover, the AJPH study that he cites was based on long-term follow-up of individuals who were uninsured in a single baseline year. While baseline-year uninsurance is a strong predictor of subsequent year uninsurance, some undoubtedly crossed over into being insured. Hence, the AJPH study estimate of the impact of uninsurance on mortality likely understates the effect.

    Kronick’s study was based on less secure data than the AJPH report. He used the NHIS, which is entirely self-reported, whereas the AJPH used the NHANES, which included physical examinations of all participants. Thus, Kronick relied on participants self-description of their health status and BMI, while the AJPH paper used measured BMI and physicians’ (as well as patients’) assessments of baseline health status.

    Professor Conover notes that in the Sommers study, the observed decline in mortality in Arizona did not achieve statistical significance. But he fails to note that the mortality increase in Maine was also non-significant, thus Conover’s comparison of Maine to New York (where Sommers found a significant mortality decrease) is a misapplication of a non-significant point estimate. We note that Sommers’ overall conclusion was “State Medicaid expansions to cover low-income adults were significantly associated with reduced mortality as well as improved coverage, access to care, and self-reported health.”

  6. Rob Stone MD Says:

    Conover is fond of quoting Avik Roy, an articulate ideologue who has argued the dubious case that Medicaid is worse than no health insurance at all (citing many weak references): The mortality effects of Medicaid coverage are obviously difficult to project and any paper will be open to methodological critiques. Data on diagnosis and treatment of depression, diabetics finally able to afford their treatment, women availing themselves of mammograms and Pap smears, and patients and families avoiding financial catastrophe still stand as an unchallenged call for Medicaid expansion being the responsible action for states to carry out.
    As a physician who has taken care of thousands of Medicaid patients in my 35 year medical career, I am acutely aware of the problems we face caring for these people. But they are people, not statistics, and deserve care. I am reminded of John Kerry’s famous question to the Senate Foreign Relations Committee in 1971, and how we might ask today, “How do you ask a person to be the last to die for a failed policy?” How can we as a nation leave tens of millions without access to healthcare? In my state of Indiana, our leaders appear to be working hard to make the political case that they should not provide healthcare to hundreds of thousands of our citizens. That is wrong.

  7. Christopher James Conover Says:

    The second study used to generate the lower bound mortality figure (7,115) is based on Himmelstein and Woolhandler’s own previous work in AJPH. Thus, they should be well aware of its limitations and flaws.

    • First and foremost, that study compared the uninsured to individuals with private insurance. In light of the mountain of evidence showing the private insurance is vastly superior to Medicaid when it comes to health outcomes, including mortality. Much of this evidence also is observational but the best studies, which take into account selection effects, show that private coverage is superior, e.g. Bhattacharya et al.’s analysis of the impact of insurance coverage on mortality for HIV/AIDS patients []. The authors conclude: “The better outcomes associated with private insurance are attributable to the more restrictive prescription drug policies of Medicaid.” Thus, extrapolation from the AJPH results is inappropriate at worse and will lead to exaggerated estimates of mortality gains at best.

    • Second, the AJPH study was an observational study, with all the attendant concerns about selection effects that might contaminate comparison. In a comprehensive review of the literature conducted in 2004, Helen Levy and David Meltzer concluded that observational studies are “not likely to provide much insight into the causal effects of health insurance on health.” []
    To illustrate, the AJPH authors did take into account many factors (age, gender, race/ethnicity, income, education, self- and physician-rated health status, body mass index, leisure exercise, smoking, and regular alcohol use) that could account for mortality differences between the 2 groups compared, but not everything. It’s notable that on every metric of health risk (obesity, lack of exercise, smoking, drinking) the uninsured led riskier lives (Table 1). So it is not at all implausible to imagine they are more prone to dying in motor vehicle accidents due to lack of seatbelt use, speeding etc. The AJPH study merely examines the uninsured and privately insured in year 1 and then measures what fraction have died 6-14 years later. It does not examine the causes of deaths, including many that would have nothing to do with health insurance.

    • Third, the AJPH study is based on only 9.004 adults. There is a far superior study done by Richard Kronick in the same year that uses a sample size of 672,526,controls for nearly the identical list of individual characteristics and has a very similar follow-up period This study is structured in the same fashion as the AJPH study but with very different results: Kronick found no statistically significant difference in mortality risk for those who were uninsured compared to those with employer-based coverage. He concluded: “The Institute of Medicine’s estimate that lack of insurance leads to 18,000 excess deaths each year is almost certainly incorrect. It is not possible to draw firm causal inferences from the results of observational analyses, but there is little evidence to suggest that extending insurance coverage to all adults would have a large effect on the number of deaths in the United States.” Since the AJPH study merely updates the IOM estimate using a different dataset, Kronick’s observation applies to it with equal force.

    That the authors should bypass a much stronger study showing no mortality benefit from private insurance in favor of a weaker study appearing to show a 40% mortality risk associated with being uninsured is a bit disturbing. That the authors fail to even mention this study or explain their rationale for selecting the one whose results they prefer is equally inexcusable.

  8. josey Says:

    Is a class action suit against these states an option??

  9. Chris Conover Says:

    This analysis is based on 2 studies. In this reply I will focus on the upper bound figure (17,104 deaths) which is based on the Sommers study (which Avik Roy has nicely picked apart here: There’s 3 big problems with the study.
    • First, the Sommers study examined 3 states, but the present analysis is based only on the aggregate finding of a weighted average mortality decrease of 19.6 per 100,000. What Dickman et al. don’t point out is that the original study showed that mortality DECREASED in NY (by 22.2/100,000 but INCREASED in ME (by 13.4/100,000); the observed decline in AZ (by 10.2/100,000) was not significant. If Medicaid truly were beneficial to health, we would not expect to see it killing people in ME or only having a demonstrable positive effect in only 1 out of 3 states studied. The aggregate result is largely driven by NY (both in terms of the size of its Medicaid population, which affects the weighted results, and the purported size of the beneficial effect on mortality).

    In terms of generalizability, the evidence is that more states are like ME than NY in terms of generosity of eligibility/benefits etc. Indeed, Public Citizen ranks NY’s Medicaid program 8th in the nation compared to ME’s ranking of #13 []. Thus, Dickman et al.”s extrapolation to the entire nation is quite inappropriate especially given that the Sommers study explicitly cautions “the results are largely driven by the largest [state] (New York), so our results may not be generalizable to other states.” In short, the researchers did precisely what Sommers et al. cautioned them not to do!

    • Second, unlike the OHIE, the Sommers study didn’t directly measure mortality risk among those with Medicaid. It compared changes in all-cause county-level mortality for adults 20-64 in 3 states that expanded Medicaid to states presumed to be good comparison states (that is, they calculated an average annual county-level mortality rate for 5 years before and 5 years after Medicaid expansion, taking into account county characteristics such as percent female, age, race, urban/rural status and socioeconomic characteristics). Sommers et al. did as sophisticated a job as possible with the data they had, but could not control for everything. Thus, for example, if the Medicaid expansion states experienced a relative reduction in deaths due to automobile accidents (e.g., due to more aggressive enforcement of drunk driving laws) or a reduction in deaths among the vast majority of adults who are not on Medicaid, all of these would have chalked up to Medicaid expansion even when it’s obvious Medicaid would have had nothing to do with such mortality reductions.

    • Third, at the link cited earlier, Avik Roy explains in great detail why the particular state selected for comparison with NY (PA) might not have been a good one. Had a different, more appropriate comparison state been selected, the estimated beneficial effect of Medicaid expansion might well have disappeared entirely.

    The bottom line: one cannot cherry-pick the Sommers’ results. If people are willing to overlook the methodological limitations to claim it “proves” Medicaid saved lives in NY, then they have to be prepared to concede that Maine’s Medicaid program evidently resulted in “excess deaths.” That should give pause to anyone arguing in favor of Medicaid expansion, especially in states much lower-ranked than Maine’s. I would hope that Drs. Woolhandler and Himmelstein would not prescribe to their patients a treatment demonstrated to have killed 1 out of every 2 who took it etc.

  10. Michael Bertaut Says:

    As a staunch supporter of the Medicaid Expansion in ALL 50 states, I am dismayed to find that such a detailed study should continue to perpetuate the myth that the Medicaid expansion is “free”.

    These authors should be well aware that just the act of adding signficant numbers of people, in some states increasing Medicaid populations by 50% or more, will require a heavy investment up-front of state revenue to enact. Not every state in the Union is a Blue State who has already expanded Medicaid by cutting reimbursements and will be able to substitute $Billions of state dollars with new federal money. The poorer states, especially the ones with Balanced Budget requirements in their Constitutions, have a different calculus to perform.

    In our state, will it be free to find and process 400,000 new people? In 10 year financial planning, is the 10% the state is responsible for not real money? And finally, if dramatically increasing the patient load in an underpaying program cause a severe shortage of medical providers or ER space, is there not cost associated with expanding that provider population that will not be borne by the Fed? The only way to increase provider participation is by either legislation or raising reimbursement rates. If the new populations drive increased reimbursement on their behalf, these same higher reimbursement rates MUST be extended to the existing Medicaid populations, which are NOT being reimbursed at 90/10, but at 60/40 or 50/50.

    The Medicaid expansion is too important to be enacted upon wishes and dreams. It will have material value to millions of Americans. Let’s at least be honest about the real cost to a state so that appropriate revenue measures can be put in place now for sustainability, instead of wishing upon a star for the next 3 years, and then worrying about fixing it later.


  11. Frederic Jones MD FACC Says:

    Opting Out Of Medicaid Expansion: The Health and Financial Impacts
    I am a just retired(at age 80) physician who completed 28 years of volunteering in free clinics staffed by volunteers and providing comprehensive care to the working poor who were not Medicaid eligible. When the ACA was passed, I was quite optimistic that the community efforts to care for the uninsured would be considerably enhanced by the new law. Some of the free clinics actually closed or reduced services in that anticipation.
    I now realize that there will be serious shortcomings in some of the states including South Carolina.
    South Carolina’s decision to opt out of Medicaid expansion may have adverse health and financial consequences.
    Despite the widely held belief that almost all Americans will be insured under the ACA, more than 32 million people will remain uninsured after the law goes into effect. Some 500,000 of our state’s residents will remain uninsured and will be at a significant disadvantage, and may forego gains in access to care, financial well-being, physical and mental health, and longevity that would be expected with expanded Medicaid coverage

  12. Mark Says:

    This study takes a very myopic view of economic/financial consequences. For example, you state that “Because the federal government will pay 100 percent of increased costs associated with Medicaid expansion for the first three years (and 90 percent thereafter), opt-out states are also turning down billions of dollars of potential revenue, which might strengthen their local economy.” This totally neglects to recognize that those federal billions must come out of the economy first – either in tax burden on the economy (locally and nationally) or in long term borrowing costs (taxes down the road.) The relevant study is how to best provide access. Medicaid expansion could easily lead to more rationing (less access) and lesser quality for all. It is wrong to assume the Medicaid model will simply scale up without negative financial/economic cost and unintended economic impacts.

  13. Liz Says:

    I live in a state that did not expand medicaid (our governor said he was “proud” not to expand it). Unfortunately I have cancer. I would have been eligible for medicaid had they expanded it. Fortunately for me I still have COBRA 5 more months. ACA insurance costs several hundred more dollars a month than COBRA with triple the out of pocket/deductible. I will not have insurance when COBRA runs out. As it is all my unemployment compensation went to COBRA health and other health care expenses. Shortly I will be homeless because I am running out of money paying for both health care and basic living expenses.

    Even if I could afford ACA it is close to useless. It has a limited local network that does not include any comprehensive cancer centers (I am currently being treated at MD Anderson) and is not accepted outside of a 4 county area. This, apparently, is a common problem with this insurance. Looking up the offering company’s “regular” networks only two companies available of which one is cheaper than the other so talking about the cheaper one), I noticed the cost is roughly the same for ACA care and the company’s “regular” individual health insurance plans that have nationwide networks. By my calculation I will need to have around $18,000 set aside to pay for premiums and out of pocket/deductible over a 12 month period. Considering, if they ever turn back on extended unemployment benefits, I only get (after 10% fed taxes are taken out) $196/week this is untenable.

    The Lymphoma and Leukemia Society just released a study of 4 states and noted that most ACA insurances are not accepted at the comprehensive or other major cancer centers. They also noted that the limited networks of these ACA insurances will greatly limit the ability of cancer patients and other chronically ill patients to access good care and/or be treated at specialty centers (See link below to the report).

    Unfortunately when I am no longer able to receive medical care because COBRA ran out I will also be likely counted in the ranks of those who died due to no access to health care… And we call ourselves a civilized country, .

  14. John Grima Says:

    I am trying to understand the time frame within which these impacts are realized. The number not insured, depression, diabetic medication, mammogram, pap smear, and mortality indicators are … all annual? The catastrophic medical expenditures figures … ?

  15. Jan Lou Says:

    This info with a Health Work Group of the (CA) Contra Costa County Advisory Council on Aging. The opt-out states will harm our senior dual eligible residents — those on both Medicare & Medicaid. CA & states which opted in to expand Medicaid will benefit financially, morally, and have healthier people.

  16. David Himmelstein Says:

    They are annual figures

  17. Karla Says:

    To the authors – Are these adverse outcomes (table 3) annual or longer timeframe (if so, what is it)? I would like to quote these figures, can’t until I know that critical piece of information.

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