Jane Tavares, UMass Boston and Marc Cohen, UMass Boston

Vice President Kamala Harris outlined a proposal to allow Medicare to expand its coverage of home health care for older Americans. The Democratic presidential nominee announced this plan on the television talk show “The View.”

Harris explained that she aimed to take the burden off members of the “sandwich generation,” who are taking care of their kids and aging parents at the same time. She said the cost of this additional paid care could be paid for with the money the government will save by negotiating with pharmaceutical companies to reduce what Medicare pays for prescription drugs.

The Conversation U.S. asked Jane Tavares and Marc Cohen, scholars of long-term care, to assess what’s known so far about the plan.

Why is long-term care significant?

Long-term services and supports are one of the most significant expenses for older adults. They range from nonmedical assistance with food preparation, bathing, dressing and other activities of daily living to medical care in a skilled nursing facility.

Today‘s 65-year-olds have a 70% chance of eventually needing some kind of long-term care as they age, and 20% will need long-term care for more than five years.

The costs associated with even one year of long-term care can prove to be unaffordable for most people. In 2023, the median yearly cost of a private room in a nursing home was US$116,796 and that of a home health care aide was $33 per hour. That’s $96,360 yearly for eight hours of daily in-home care.

The National Council on Aging has found that 80% of older adults would be unable to absorb a financial shock — such as the need for long-term care — without impoverishing themselves. The council noted that 20% of older adults had no assets at all, and another 60% would not be able to afford more than two years of either nursing home care or care in their own homes. The average length of a long-term care stay is just over three years.

Medicare currently does not cover any long-term care, but it does cover short-term professional in-home care for recovery after a qualifying illness or injury for up to 21 days and a maximum of 100 days in a skilled nursing facility after a qualifying hospital stay.

Medicaid currently covers about 61% of the country’s total long-term care costs, over 70% of which are for home-based services. However, Medicaid has strict income and asset eligibility requirements. Although Medicaid eligibility and coverage vary by state, those who qualify for the program are at or near the federal poverty level and have less than $2,000 in individual assets, or $3,000 as a couple.

Only 15% of Americans who were 65 and older were covered by Medicaid as of 2022.

Adding to the challenge, there is a shortage of long-term care workers. In 2022, about 700,000 people were on Medicaid waitlists for home- and community-based services, and 10% of those with skilled medical needs were waiting in hospitals for spots to open in nursing homes.

What would be the impact of increasing the number of older people getting care?

An estimated 77% of older Americans desire to stay in their homes as they age, but 1 in 5 need assistance with activities of daily living. With the high costs of long-term care and few coverage options, unpaid family caregivers typically provide this care.

Expanding Medicare coverage to include professional in-home long-term care, as Harris proposes, would make it easier for older adults to stay in their homes without impoverishing themselves. It could also help alleviate burdens born by unpaid family caregivers.

Although it will depend on details that weren’t immediately available, expanding long-term care coverage beyond the people who are enrolled in Medicaid has the potential to help many vulnerable older adults.

For example, getting professional assistance with eating or bathing could prevent health complications associated with malnutrition or poor hygiene. And this care would not be at the expense of a family caregiver who might otherwise have to leave their job or take on additional physical and mental stress to provide that care.

How much will this cost the government?

Clearly, the costs associated with any new program depend on many factors. The most important are who qualifies for the program, the circumstances under which they can get benefits, and how generous those benefits are.

Harris has indicated that the new Medicare home care benefit she’s proposing would be paid for by the savings from reductions in Medicare drug costs. A relatively recent estimate for that savings in 2026 is $6.3 billion. If this is the primary way to pay for the program, it could finance only a very modest home-care benefit.

Other long-term care proposals put forward by researchers and policymakers look at increasing the Medicare tax to pay for expanding access to this benefit. Here again, how much money needs to be raised depends on how comprehensive the program would be. Researchers at the Brookings Institution estimated that making long-term care more widely available to people covered by Medicare would probably cost about $40 billion.

Why hasn’t Medicare covered in-home care until now?

When it was originally launched in 1966, the Medicare program was intended to cover acute medical care services. At that time, life expectancy was lower than it is today – meaning that fewer Americans over 65 were eligible for its benefits and would live long enough to require long-term care.

In the following six decades, no public insurance program like Medicare has emerged to help people pay for this care.

But as far back as 1994, lawmakers were drafting proposals to cover long-term care. More recently, legislators have introduced bills that could fill this gap. However, many prior efforts have failed due to a lack of agreement on how to pay for these benefits and whether everyone should be eligible, or just low-income people.

Because the federal government hasn’t stepped up, some states have introduced their own policies.

Washington state is the furthest along in this effort. It has created a public long-term care insurance program where working Washington residents contribute a small percentage of their income into the fund and can then access earned benefits to pay for services. However, due to a ballot measure that Washington voters will weigh in on during the November 2024 elections, the program may become voluntary. We believe that letting people opt out would likely make that program unsustainable.

California has also made headway, completing two feasibility studies to examine the potential of a statewide long-term care insurance program. In 2024, California also eliminated the financial asset limits for Medicaid eligibility to help expand the program so it can cover more of the state’s older residents.

Jane Tavares, Senior Research Fellow and Lecturer of Gerontology, LeadingAge LTSS Center @UMass Boston, UMass Boston and Marc Cohen, Mel King Fellow, MIT CoLab; Clinical Professor of Gerontology and Co-Director LeadingAge LTSS Center, UMass Boston

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Kenneth Evans, Rice University

For the first time in American history, quantum computing was mentioned by a candidate during a presidential debate, on Sept. 10, 2024. After Vice President Kamala Harris brought up quantum technology, she and former President Donald Trump went on to have a heated back-and-forth about American chipmaking and China’s rise in semiconductor manufacturing. Science and technology policy usually takes a back seat to issues such as immigration, the economy and health care during election season.

What’s changed for 2024?

From COVID-19 to climate change, ChatGPT to, yes, quantum computers, science-related issues are on the minds of American policymakers and voters alike. The federal government spends nearly US$200 billion each year on scientific research and development to address these challenges and many others. Presidents and Congress, however, rarely agree on how – and how much – money should be spent on science.

With the increasing public focus on global competitiveness, the climate crisis and artificial intelligence, a closer look at Trump’s and Harris’ records on science and technology policy could provide a hint about how they’d approach these topics if elected this fall.

Two distinct visions for science funding

If politics can be described as “who gets what and when,” U.S. science and technology policy can be assessed through the annual budget process for R&D. By this measure, the differences between the Trump and Biden-Harris administrations couldn’t be starker.

In his first budget request to Congress, in 2017, Trump spurned decades of precedent, proposing historic cuts across nearly every federal science agency. In particular, Trump targeted climate-related programs at the Department of Energy, the National Oceanic and Atmospheric Administration and the Environmental Protection Agency.

Trump’s fiscal policy took a page from Reagan-era conservative orthodoxy, prioritizing military spending over social programs, including R&D. Unlike Reagan, however, Trump also took aim at basic research funding, an area with long-standing bipartisan support in Congress. His three subsequent budget proposals were no different: across-the-board reductions to federal research programs, while pushing for increases to defense technology development and demonstration projects.

Congress rebuked nearly all of Trump’s requests. Instead, it passed some of the largest increases to federal R&D programs in U.S. history, even before accounting for emergency spending packages funded as part of the government’s pandemic response.

In contrast, the Biden-Harris administration made science and innovation a centerpiece of its early policy agenda – with budgets to match. Leveraging the slim Democratic majority during the 117th Congress, Biden and Harris shepherded three landmark bills into law: the Infrastructure Investment and Jobs Act, the Inflation Reduction Act and the CHIPS and Science Act. These laws contain significant R&D provisions focused on environmental projects (IIJA), clean energy (IRA) and American semiconductor manufacturing (CHIPS).

CHIPS set up programs within the National Science Foundation and the Department of Commerce to create regional technology hubs in support of American manufacturing. The act also set ambitious funding targets for federal science agencies, especially at NSF, calling for its budget to be doubled from $9 billion to over $18 billion over the course of five years.

Despite its initial push for R&D, the Biden-Harris administration’s final two budget proposals offered far less to science. Years of deficit spending and a new Republican majority in the House cast a cloud of budget austerity over Congress. Instead of moving toward doubling NSF’s budget, the agency suffered an 8% decrease in fiscal year 2024 – its biggest cut in over three decades. For FY2025, which runs from Oct. 1, 2024, through Sept. 30, 2025, Biden and Harris requested a meager 3% increase for NSF, billions of dollars short of CHIPS-enacted spending levels.

An emerging consensus on China

On technology policy, Biden and Harris share more with Trump than they let on.

Their approach to competing with China on tech follows Trump’s lead: They’ve expanded tariffs on Chinese goods and severely limited China’s access to American-made computer chips and semiconductor manufacturing equipment.

Biden and Harris have also ramped up research security efforts intended to protect U.S. ideas and innovation from China. Trump launched the China Initiative as an attempt to stop the Chinese government from stealing American research. The Biden-Harris administration ended the program in 2022, but pieces of it remain in place. Scientific collaborations between the United States and China continue to decline, to the detriment of American scientific leadership.

The Biden-Harris administration has also drawn from Trump-era policy to strengthen America’s leadership in “industries of the future.” The term, coined by Trump’s then-chief science adviser Kelvin Droegemeier, refers to five emerging technology areas: AI, quantum science, advanced manufacturing, advanced communications and biotechnology. This language has been parroted by the Biden-Harris administration as part of its focus on American manufacturing and throughout Harris’ campaign, including during the debate.

In short, both candidates align with the emerging Washington bipartisan consensus on China: innovation policy at home, strategic decoupling abroad.

Science advice not always a welcome resource

Trump’s dismissal of and at times outright contempt for scientific consensus is well documented. From “Sharpiegate,” when he mapped his own projected path for Hurricane Dorian, to pulling out of the Paris climate agreement, World Health Organization and the Iran nuclear deal, Trump has demonstrated an unwillingness to accept any advice, let alone from scientists.

Indeed, Trump took over two years to hire Droegemeier as director of the White House Office of Science and Technology Policy, or OSTP, doubling the previous record for the length of time a president has gone without a scientific adviser. This absence was no doubt reflected in Trump’s short-on-science budget requests to Congress, especially during the beginning of his administration.

On the other hand, the Biden-Harris administration has promoted science and innovation as a core part of its broader economic policy agenda. It elevated the role of OSTP: Biden is the first president to name his science adviser – a position currently held by Arati Prabhakar – as a member of his Cabinet.

By law, the president is required to appoint an OSTP director. But it is up to the president to decide how and when to use their advice. If the new White House wants the U.S. to remain a global leader in R&D, the science adviser will need to continue to fight for it.

Kenneth Evans, Scholar in Science and Technology Policy, Baker Institute for Public Policy, Rice University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Filippo Menczer, Indiana University

Foreign influence campaigns, or information operations, have been widespread in the run-up to the 2024 U.S. presidential election. Influence campaigns are large-scale efforts to shift public opinion, push false narratives or change behaviors among a target population. Russia, China, Iran, Israel and other nations have run these campaigns by exploiting social bots, influencers, media companies and generative AI.

At the Indiana University Observatory on Social Media, my colleagues and I study influence campaigns and design technical solutions – algorithms – to detect and counter them. State-of-the-art methods developed in our center use several indicators of this type of online activity, which researchers call inauthentic coordinated behavior. We identify clusters of social media accounts that post in a synchronized fashion, amplify the same groups of users, share identical sets of links, images or hashtags, or perform suspiciously similar sequences of actions.

We have uncovered many examples of coordinated inauthentic behavior. For example, we found accounts that flood the network with tens or hundreds of thousands of posts in a single day. The same campaign can post a message with one account and then have other accounts that its organizers also control “like” and “unlike” it hundreds of times in a short time span. Once the campaign achieves its objective, all these messages can be deleted to evade detection. Using these tricks, foreign governments and their agents can manipulate social media algorithms that determine what is trending and what is engaging to decide what users see in their feeds.

Generative AI

One technique increasingly being used is creating and managing armies of fake accounts with generative artificial intelligence. We analyzed 1,420 fake Twitter – now X – accounts that used AI-generated faces for their profile pictures. These accounts were used to spread scams, disseminate spam and amplify coordinated messages, among other activities.

We estimate that at least 10,000 accounts like these were active daily on the platform, and that was before X CEO Elon Musk dramatically cut the platform’s trust and safety teams. We also identified a network of 1,140 bots that used ChatGPT to generate humanlike content to promote fake news websites and cryptocurrency scams.

In addition to posting machine-generated content, harmful comments and stolen images, these bots engaged with each other and with humans through replies and retweets. Current state-of-the-art large language model content detectors are unable to distinguish between AI-enabled social bots and human accounts in the wild.

Model misbehavior

The consequences of such operations are difficult to evaluate due to the challenges posed by collecting data and carrying out ethical experiments that would influence online communities. Therefore it is unclear, for example, whether online influence campaigns can sway election outcomes. Yet, it is vital to understand society’s vulnerability to different manipulation tactics.

In a recent paper, we introduced a social media model called SimSoM that simulates how information spreads through the social network. The model has the key ingredients of platforms such as Instagram, X, Threads, Bluesky and Mastodon: an empirical follower network, a feed algorithm, sharing and resharing mechanisms, and metrics for content quality, appeal and engagement.

SimSoM allows researchers to explore scenarios in which the network is manipulated by malicious agents who control inauthentic accounts. These bad actors aim to spread low-quality information, such as disinformation, conspiracy theories, malware or other harmful messages. We can estimate the effects of adversarial manipulation tactics by measuring the quality of information that targeted users are exposed to in the network.

We simulated scenarios to evaluate the effect of three manipulation tactics. First, infiltration: having fake accounts create believable interactions with human users in a target community, getting those users to follow them. Second, deception: having the fake accounts post engaging content, likely to be reshared by the target users. Bots can do this by, for example, leveraging emotional responses and political alignment. Third, flooding: posting high volumes of content.

Our model shows that infiltration is the most effective tactic, reducing the average quality of content in the system by more than 50%. Such harm can be further compounded by flooding the network with low-quality yet appealing content, thus reducing quality by 70%.

Curbing coordinated manipulation

We have observed all these tactics in the wild. Of particular concern is that generative AI models can make it much easier and cheaper for malicious agents to create and manage believable accounts. Further, they can use generative AI to interact nonstop with humans and create and post harmful but engaging content on a wide scale. All these capabilities are being used to infiltrate social media users’ networks and flood their feeds with deceptive posts.

These insights suggest that social media platforms should engage in more – not less – content moderation to identify and hinder manipulation campaigns and thereby increase their users’ resilience to the campaigns.

The platforms can do this by making it more difficult for malicious agents to create fake accounts and to post automatically. They can also challenge accounts that post at very high rates to prove that they are human. They can add friction in combination with educational efforts, such as nudging users to reshare accurate information. And they can educate users about their vulnerability to deceptive AI-generated content.

Open-source AI models and data make it possible for malicious agents to build their own generative AI tools. Regulation should therefore target AI content dissemination via social media platforms rather then AI content generation. For instance, before a large number of people can be exposed to some content, a platform could require its creator to prove its accuracy or provenance.

These types of content moderation would protect, rather than censor, free speech in the modern public squares. The right of free speech is not a right of exposure, and since people’s attention is limited, influence operations can be, in effect, a form of censorship by making authentic voices and opinions less visible.

Filippo Menczer, Professor of Informatics and Computer Science, Indiana University

This article is republished from The Conversation under a Creative Commons license. Read the original article.