US Federal Program Research Articles

Editorial: Effective Direct-Acting Antiviral Treatments Support Global and National Programs to Eliminate Hepatitis C

Eliminating an infectious disease aims to result in no residual disease in a specific geographic area due to deliberate efforts, which may require ongoing control measures to prevent the re-establishment of infection transmission. Currently, no effective vaccines prevent hepatitis C virus (HCV) infection. However, during the past decade, oral direct-acting antivirals (DAAs) have been developed and approved for the treatment of HCV that result in a 'cure' in more than 95% of people infected. Morbidity and mortality from untreated hepatitis C result from liver failure, cirrhosis, and HCC and can be prevented by curative treatment with DAAs, which also prevents HCV transmission. Morbidity and mortality from untreated hepatitis C result from liver failure, cirrhosis, and HCC and can be prevented by curative treatment with DAAs, which also prevents HCV transmission. In May 2016, the World Health Assembly of the World Health Organization (WHO) proposed the first global health initiative on viral hepatitis, which proposed the elimination of hepatitis B and C by 2030. In March 2023, the US President announced that in the 2024 fiscal year budget proposal, a 5-year program was approved to eliminate hepatitis C in the US, using a screening and treatment approach. This Editorial aims to present the development of effective and curative DAA treatments for hepatitis C that support the WHO and US Federal programs for disease elimination.

German Pharmaceutical Pricing: Lessons for the United States.

To control pharmaceutical spending and improve access, the United States could adopt strategies similar to those introduced in Germany by the 2011 German Pharmaceutical Market Reorganization Act. In Germany, manufacturers sell new drugs immediately upon receiving marketing approval. During the first year, the German Federal Joint Committee assesses new drugs to determine their added medical benefit. It assigns them a score indicating their added benefit. New drugs comparable to drugs in a reference price group are assigned to that group and receive the same reimbursement, unless they are therapeutically superior. The National Association of Statutory Health Insurance Funds then negotiates with manufacturers the maximum reimbursement starting the 13th month, consistent with the drug's added benefit assessment and price caps in other European countries. In the absence of agreement, an arbitration board sets the price. Manufacturers accept the price resolution or exit the market. Thereafter, prices generally are not increased, even for inflation. US public and private insurers control prices in diverse ways, but typically obtain discounts by designating certain drugs as preferred and by restricting patient access or charging high copayment for nonpreferred drugs. This article draws 10 lessons for drug pricing reform in US federal programs and private insurance.

Education without evidence: Gaps in data availability for refugee, asylee, and humanitarian migrant students in US schools

The collection and use of empirical data for educational decision-making in the United States is required by the Every School Succeeds Act (ESSA) and the Results Driven Accountability mandate, but there is insufficient data available for one population, in particular: refugee, asylee, and humanitarian migrant (RAHM) students. As a result, the terms of these US federal programs are violated by all states, districts, and campuses who count refugee, asylee, or humanitarian migrant students among their populations. Evidence suggests that almost no standardized, publicly-available educational data is available for refugee, asylee, and humanitarian migrant students in the US, but what publicly-available and reported data is available is either so localized so that it can be neither replicated nor generalized or so aggregated that it cannot be disaggregated for analysis and decision-making.

The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry.

Physical chemistry stands today at an exciting transition state where the integration of machine learning and data science tools into all corners of the field stands poised to do nothing short of revolutionizing the discipline. These powerful techniques—when appropriately combined with domain knowledge, tools, and expertise—have led to new physical insights, better understanding, accelerated discovery, rational design, and inverse engineering that transcend traditional approaches to materials, molecular, and chemical science and engineering. The primary driver of this trend has been the impressive advances enabled by machine learning, artificial intelligence, and data science tools, ranging from the discovery of novel electronic and optical materials by high-throughput virtual screening, to the massive acceleration of molecular simulations using learned classical force fields with quantum accuracy, to the powering of “self-driving laboratories” for automated chemical discovery. The 2011 White House Materials Genome Initiative (MGI), the 2017 NSF Data-Driven Discovery Science in Chemistry (D3SC) initiative, and the 2019 NSF Big Idea Harnessing the Data Revolution are some of the US federal programs that have provided incentive, attention, momentum, and support to power these advances and help drive the field forward. Necessity is also the mother of invention, and the prevalence of large data sets routinely generated by high-throughput virtual screening or automated experimentation have spurred the need for scalable data science and machine learning techniques to parse, explore, and harness the full power of these voluminous data streams. It bears remembering that physical chemistry is no stranger to machine learning, most visibly in the cheminformatics and quantitative structure property relation (QSPR) work that emerged in the 1980s. Some of the techniques being implemented today are, to some degree, reinventions of these ideas, but others are fundamentally new concepts that have been adopted and adapted from diverse fields including computer vision, manifold learning, and deep learning. This Virtual Special Issue on Machine Learning in Physical Chemistry covering all sections of The Journal of Physical Chemistry A/B/C pays tribute to this development, and the relevance and popularity of this topic is reflected in the depth and breadth of excellent articles in this exciting collection.

The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry.

Physical chemistry stands today at an exciting transition state where the integration of machine learning and data science tools into all corners of the field stands poised to do nothing short of revolutionizing the discipline. These powerful techniques—when appropriately combined with domain knowledge, tools, and expertise—have led to new physical insights, better understanding, accelerated discovery, rational design, and inverse engineering that transcend traditional approaches to materials, molecular, and chemical science and engineering. The primary driver of this trend has been the impressive advances enabled by machine learning, artificial intelligence, and data science tools, ranging from the discovery of novel electronic and optical materials by high-throughput virtual screening, to the massive acceleration of molecular simulations using learned classical force fields with quantum accuracy, to the powering of “self-driving laboratories” for automated chemical discovery. The 2011 White House Materials Genome Initiative (MGI), the 2017 NSF Data-Driven Discovery Science in Chemistry (D3SC) initiative, and the 2019 NSF Big Idea Harnessing the Data Revolution are some of the US federal programs that have provided incentive, attention, momentum, and support to power these advances and help drive the field forward. Necessity is also the mother of invention, and the prevalence of large data sets routinely generated by high-throughput virtual screening or automated experimentation have spurred the need for scalable data science and machine learning techniques to parse, explore, and harness the full power of these voluminous data streams. It bears remembering that physical chemistry is no stranger to machine learning, most visibly in the cheminformatics and quantitative structure property relation (QSPR) work that emerged in the 1980s. Some of the techniques being implemented today are, to some degree, reinventions of these ideas, but others are fundamentally new concepts that have been adopted and adapted from diverse fields including computer vision, manifold learning, and deep learning. This Virtual Special Issue on Machine Learning in Physical Chemistry covering all sections of The Journal of Physical Chemistry A/B/C pays tribute to this development, and the relevance and popularity of this topic is reflected in the depth and breadth of excellent articles in this exciting collection.

The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry

Physical chemistry stands today at an exciting transition state where the integration of machine learning and data science tools into all corners of the field stands poised to do nothing short of revolutionizing the discipline. These powerful techniques—when appropriately combined with domain knowledge, tools, and expertise—have led to new physical insights, better understanding, accelerated discovery, rational design, and inverse engineering that transcend traditional approaches to materials, molecular, and chemical science and engineering. The primary driver of this trend has been the impressive advances enabled by machine learning, artificial intelligence, and data science tools, ranging from the discovery of novel electronic and optical materials by high-throughput virtual screening, to the massive acceleration of molecular simulations using learned classical force fields with quantum accuracy, to the powering of “self-driving laboratories” for automated chemical discovery. The 2011 White House Materials Genome Initiative (MGI), the 2017 NSF Data-Driven Discovery Science in Chemistry (D3SC) initiative, and the 2019 NSF Big Idea Harnessing the Data Revolution are some of the US federal programs that have provided incentive, attention, momentum, and support to power these advances and help drive the field forward. Necessity is also the mother of invention, and the prevalence of large data sets routinely generated by high-throughput virtual screening or automated experimentation have spurred the need for scalable data science and machine learning techniques to parse, explore, and harness the full power of these voluminous data streams. It bears remembering that physical chemistry is no stranger to machine learning, most visibly in the cheminformatics and quantitative structure property relation (QSPR) work that emerged in the 1980s. Some of the techniques being implemented today are, to some degree, reinventions of these ideas, but others are fundamentally new concepts that have been adopted and adapted from diverse fields including computer vision, manifold learning, and deep learning. This Virtual Special Issue on Machine Learning in Physical Chemistry covering all sections of The Journal of Physical Chemistry A/B/C pays tribute to this development, and the relevance and popularity of this topic is reflected in the depth and breadth of excellent articles in this exciting collection.

Monetising the savings of remotely sensed data and information in Burn Area Emergency Response (BAER) wildfire assessment

We used a value of information approach to demonstrate the cost-effectiveness of using satellite imagery as part of the Burn Area Emergency Response (BAER), a US federal program that identifies imminent post-wildfire threats to human life and safety, property and critical natural or cultural resources. We compared the costs associated with producing a Burn Area Reflectance Classification map and implementing a BAER when imagery from satellites (either Landsat or a commercial satellite) was available to when the response team relied on information collected solely by aerial reconnaissance. The case study included two evaluations with and without Burn Area Reflectance Classification products: (a) savings of up to US$51000 for the Elk Complex wildfire incident request and (b) savings of a multi-incident map production program. Landsat is the most cost-effective way to input burn severity information into the BAER program, with savings of up to US$35 million over a 5-year period.

Interwoven support: An historical survey of US federal programs enabling immunization

The US Government (USG) can date its involvement with immunization to military and civilian efforts in 1777 and 1813 to prevent smallpox. USG involvement began accelerating with federal licensing of vaccine and antibody manufacturers in 1903. In addition to ongoing regulation of manufacturing and product quality, military and civilian arms of the USG have led research efforts into new or improved vaccines. These efforts have included diseases endemic in the United States, as well as medical countermeasures targeted against biological weapons, influenza pandemics, and emerging infectious diseases. Especially since the 1950s, the USG has provided increasing levels of funding to purchase vaccines and conduct vaccination programs. These programs have focused largely on children, although vaccination programs for adults have been expanded somewhat in recent years. Multiple agencies of the USG have convened various panels of accomplished external experts who have generated widely regarded recommendations on vaccine safety and efficacy and optimal immunization practices. USG programs for safety assessment, injury compensation, liability protection, and disease surveillance have been developed to assess needs, evaluate safety questions, ensure vaccine supply, and foster confidence in vaccination efforts. Debates on the extent of government involvement date back to the 1890s and continue today. Several pivotal expansions of government involvement followed disease outbreaks or manufacturing accidents. This historical survey describes each of the major US federal programs in these categories, including references to applicable law.

Republic of Palau

Republic of Palau Donald R. Shuster (bio) President Johnson Toribiong's third year in office was very busy. One of the most important issues was the finalization of negotiations of the Amended Compact of Free Association with the United States. This was done in early September when Toribiong and Deputy Assistant Secretary of State, Bureau of East Asian and Pacific Affairs Frankie Reed signed a six-page, ten-part agreement with funding support totaling $229 million for fiscal years 2011-2024. It included an additional estimated $36 million in US federal programs and services, but without a cap on the total amount of federal dollars available. Initially there was hope that the US Congress would pass the agreement before the end of 2010, but Mr Toribiong was not able to testify before Congress until June 2011. While his testimony was respectful and comprehensive, he made a case for special consideration by mentioning Palauans serving and making the ultimate sacrifice in the United States' conflicts in Iraq and Afghanistan, Palau's nearly unanimous support for the United States in United Nations voting, and overtures made to Palau by the People's Republic of China. He said, "We all want greater economic interaction with China, but it should be without compromising the close alliance between Palau and the United States." In his testimony before the US Senate, he strongly urged support from the Committee on Energy and Natural Resources and from Congress as a whole. At the time of this review, Congress had not acted on the amended compact agreement. President Toribiong hosted two important regional meetings in 2010: one with the Micronesian presidents in July and one with the Micronesian chief executives in December. A communiqué was issued for the first meeting and a 225-page proceedings document for the second. Three key issues addressed at both meetings were the high rates of non-communicable diseases, the depletion of Micronesia's fisheries resources, and adaptation to climate change in the islands. It is generally acknowledged that the pillars of good governance include a public auditor, a special prosecutor, [End Page 163] a free and independent press, and a competent, independent judiciary. By mid-year, Palau had no public auditor (although it has had an acting auditor for over ten years) and no special prosecutor. Furthermore, the president had fired the special counsel hired to prosecute those responsible for the collapse of the Pacific Savings Bank (the so-called People's Bank) in 2006. In February 2011 the president appointed Assistant Attorney General Lucketta McMahon as the new independent counsel. However, McMahon is new to Palau and very new to the savings bank collapse. President Toribiong is a superb speaker and appeared before the UN General Assembly. Mentioning Palau's peacekeeping efforts in Darfur, Timor Leste, and Solomon Islands, he also reported on his nation's efforts to maintain the viability of Pacific fish stocks by limiting purse seine activity as well as on the work being done by the Pacific Small Island Developing States to persuade the world that climate change is not merely a development issue but also a security issue. Toribiong called on the UN Security Council to include Japan in its ranks and requested that the world body also include Taiwan in select UN activities. In response to the president's appeal to the world's conscience, Germany volunteered to assist in the removal of explosives left over from World War II battles on Palau's southern islands of Peleliu and Angaur. In January 2011, the president delivered special remarks to the World League for Freedom and Democracy in Taipei, Republic of China. In those remarks he stressed how his nation has demonstrated its dedication to the principles enshrined in the Universal Declaration of Human Rights. Finally, Toribiong addressed the graduates of Palau Community College in late May, delivering a very personal message that emphasized goals and passion as necessary for success in any endeavor. It was a grand speech that mixed Palauan proverbs with personal experiences. In April, the president presented his third progress report, known as the State of the Republic Address. It was comprehensive and laudatory of his ministries. The president announced that the compact...

The Great Social Security Debate

Social security is the largest US federal program and the most enduring legacy of the New Deal. The program has more than 50 million beneficiaries and generates expenditures (US$585 billion in 2007) that are significantly higher than the defence budget.1 Known as the third rail of American politics (touch it and die), social security is a popular program, which means that few politicians explicitly seek to dismantle it. Yet, because of the demographic challenge ahead, some changes are necessary to fix the anticipated long-term fiscal imbalance in social security. The current debate on social security is about the nature of the changes necessary to solve the long-term fiscal challenge stemming from demographic aging. This brief article explores the debate on the future of social security, with a focus on the Bush years (2001-09) and the ideas put forward by Barack Obama during the 2008 presidential campaign.THE DEVELOPMENT OF SOCIAL SECURITYThe modern pension system in the United States took shape during the New Deal and the post-World War II era. This system is divided into four main parts: federal old-age, survivors, and disability insurance - a centralized earnings-related scheme that covers more than 95 percent of the workforce; supplemental security income - a means-tested federal assistance program offering modest benefits to poor, disabled, and elderly citizens; voluntary, tax-subsidized private pension plans (defined-benefit and definedcontribution schemes included); and personal savings and tax-subsidized individual retirement accounts.Adopted in 1935 as a key element of the social security act, old-age, survivor, and disability insurance is widely known as social security. Now the largest social program in the United States, social security expanded in the postwar era, especially during the Nixon presidency (1969-74). In the mid1970s, the economic downturn triggered by the first oil crisis and the overly generous indexation system implemented in 1974 created the first major fiscal crisis in social security. As a response, the Carter administration sponsored the enactment of the 1977 amendments to the social security act, which raised the payroll tax and changed the indexation formula in order to reduce the anticipated benefits of future retirees. Because the 1977 amendments failed to truly solve the fiscal crisis, in 1983 congress adopted a set of seemingly technical changes aimed at solving the new fiscal crisis in social security. During the legislative process, a rise in the retirement age from 65 to 67 taking place incrementally between the years 2000 and 2022 was added to the bill, inspired by the report of a bipartisan commission on social security that President Reagan had created two years earlier. Overall, the 1983 legislation preserved the basic characteristics of the program, which remained a defined-benefit scheme.2During the 1990s, actuarial previsions became more favourable as economic growth boosted the trust fund's reserves. According to the current actuarial projections, this trust fund should accumulate surpluses until the early 2010s. In the long run, however, population aging and the retirement of baby boomers would further increase the percentage of elderly people in the US. Compared with only 4.1 percent in 1900, this percentage should increase from 12.4 to 20 between 2000 and 2030.3 In such a context, expenditures should exceed revenues, thus eroding social security reserves. According to the 2008 trustees' report, the fund would face fiscal shortfalls in 2042.4 Although liberal policy experts argue that relatively technical changes could solve this long-term fiscal challenge, many conservatives have called for a major transformation of social security.5 Although there is no evidence that such a project would help address the long-term fiscal imbalance in the program, demographic fears concerning its future are central to the campaign in favour of social security privatization. …

Savings Incentives for Low-and Moderate-Income Families in the United States: Why is the Saver's Credit Not More Effective?

This paper uses data from the largest tax preparer in the United States to estimate the impact of the “saver’s credit,” a US federal program providing financial incentives to encourage retirement savings, on the decision to contribute to an IRA. It finds significant, but very modest, effects. This is contrasted with results from a field experiment showing much larger impacts of clearly presented matching incentives. Various explanations are discussed for why the saver’s credit is not more effective. (JEL: H00, H31, C93, D14)

Technology Transfer and Economic Growth

This paper presents the economic framework supporting the conclusion that US federal programmes, such as the Bayh–Dole Act of 1980, which increase the pay-off from research and development funding (R&D) can be effective agents of economic growth. A review of the literature in this field provides evidence that links investment in research to economic growth. By modifying the traditional Cobb–Douglas production function to include a research and development input, in addition to the capital and labour input, this study defines how multi-factor productivity (MFP) growth is controlled by the interaction of R&D and its commercialization. The combined contribution to MFP growth is defined as the product of the elasticity of output for R&D and the rate of growth of the R&D input. Evidence supporting the importance of the elasticity component for multi-factor productivity growth is presented, and the study then concludes that programmes to increase the elasticity of output for R&D – what is referred to as increasing the pay-off from R&D – may be an effective means of realizing a larger return on the investment in R&D.

Public Policy in Appalachia: The Application of a Neglected Geographical Factor?

The public policy process is defined, and its importance as a factor in geographical change is evaluated, in the context of a study of the impact of US federal programmes in Appalachia, with special reference to the Appalachian Regional Development Act of 1965. The significance of each stage in the public policy process is analyzed, beginning with problem identification; proceeding through the threshold of political awareness to the generation of a popular political issue; following the formulation of legislation, the stages leading to its becoming law and the operation of its provisions with further modifications over the years; and finally assessing the impact of these stages on geographical change within Appalachia.

Residential energy conservation: Analysis of US federal programmes

The authors examine three residential energy conservation programmes under study and development by the US government. These programmes include setting appliance efficiency targets, developing new thermal standards for housing, retrofitting existing houses to improve thermal performance and a stronger energy conservation programme. The programmes are compared using a model developed for the FEA and ERDA which projects consumption up to the year 2000. The major conclusion is that the programmes can save both energy for the USA and money for individual households.