US Spending on Personal Health Care and Public Health, 1996-2013.

Importance: US health care spending has continued to increase, and now accounts for more than 17% of the US economy. Despite the size and growth of this spending, little is known about how spending on each condition varies by age and across time. Objective: To systematically and comprehensively estimate US spending on personal health care and public health, according to condition, age and sex group, and type of care. Design and Setting: Government budgets, insurance claims, facility surveys, household surveys, and official US records from 1996 through 2013 were collected and combined. In total, 183 sources of data were used to estimate spending for 155 conditions (including cancer, which was disaggregated into 29 conditions). For each record, spending was extracted, along with the age and sex of the patient, and the type of care. Spending was adjusted to reflect the health condition treated, rather than the primary diagnosis. Exposures: Encounter with US health care system. Main Outcomes and Measures: National spending estimates stratified by condition, age and sex group, and type of care. Results: From 1996 through 2013, $30.1 trillion of personal health care spending was disaggregated by 155 conditions, age and sex group, and type of care. Among these 155 conditions, diabetes had the highest health care spending in 2013, with an estimated $101.4 billion (uncertainty interval [UI], $96.7 billion-$106.5 billion) in spending, including 57.6% (UI, 53.8%-62.1%) spent on pharmaceuticals and 23.5% (UI, 21.7%-25.7%) spent on ambulatory care. Ischemic heart disease accounted for the second-highest amount of health care spending in 2013, with estimated spending of $88.1 billion (UI, $82.7 billion-$92.9 billion), and low back and neck pain accounted for the third-highest amount, with estimated health care spending of $87.6 billion (UI, $67.5 billion-$94.1 billion). The conditions with the highest spending levels varied by age, sex, type of care, and year. Personal health care spending increased for 143 of the 155 conditions from 1996 through 2013. Spending on low back and neck pain and on diabetes increased the most over the 18 years, by an estimated $57.2 billion (UI, $47.4 billion-$64.4 billion) and $64.4 billion (UI, $57.8 billion-$70.7 billion), respectively. From 1996 through 2013, spending on emergency care and retail pharmaceuticals increased at the fastest rates (6.4% [UI, 6.4%-6.4%] and 5.6% [UI, 5.6%-5.6%] annual growth rate, respectively), which were higher than annual rates for spending on inpatient care (2.8% [UI, 2.8%-2.8%] and nursing facility care (2.5% [UI, 2.5%-2.5%]). Conclusions and Relevance: Modeled estimates of US spending on personal health care and public health showed substantial increases from 1996 through 2013; with spending on diabetes, ischemic heart disease, and low back and neck pain accounting for the highest amounts of spending by disease category. The rate of change in annual spending varied considerably among different conditions and types of care. This information may have implications for efforts to control US health care spending.

Systematic evaluation of personal genome services for Japanese individuals.

Disease risk prediction (DRP) is one of the most important challenges in personal genome research. Although many direct-to-consumer genetic test (DTC) companies have begun to offer personal genome services for DRP, there is still no consensus on what constitutes a gold-standard service. Here, we systematically evaluated the distributions of DRPs from three DTC companies, that is, 23andMe, Navigenics and deCODEme, for 22 diseases using three Japanese samples. We systematically quantified and analyzed the differences between each DTC company's DRPs. Our independency test showed that the overall prediction results were correlated with each other, but not perfectly matched; less than onethird mismatching of the opposite direction occurred in eight diseases. Moreover, we found that the differences could mainly be attributed to four factors: (1) single nucleotide polymorphism (SNP) selection, (2) average risk estimation, (3) the disease risk calculation algorithm and (4) ethnicity adjustment. In particular, only 7.1% of SNPs over 22 diseases were reviewed by all three companies. Therefore, development of a universal core SNPs list for non-Caucasian samples will be important for achieving better prediction capacity for Japanese samples. This systematic methodology provides useful insights for improving the capacity of DRPs in future personal genome services.

What really matters in cancer?: Putting people back into the heart of cancer policy.

Twenty-first century cancer is framed by the biomedical revolution. The cancer patient today enters a world dominated by the success and failures of biomedical science from gene to statistic. By any measure, bibliometric, financial, media profile or simply the lingua franca of everyday discourse about cancer, biomedicine occupies the majority of the cultural discourse around cancer. Yet the reality is that patients are people who are diagnosed, treated, survive and die with cancer in a world bounded by their personal and social experiences. Others they have known with the disease shape their values and concepts. The social determinants of cancer are of far greater importance in terms of prevention, treatment and care than is acknowledged in policy terms. In light of the demographic trend of a rapidly ageing population, increasing costs of healthcare and the urgent need to be able to deliver affordable cancer care set against the population's almost insatiable ability to absorb all types of healthcare, there is an urgent need to redress policy balance in terms of both understanding the social determinants of cancer and bringing new insights into evidence-based national cancer planning and delivery of services.

Waiting times for diagnosis and treatment of head and neck cancer in Denmark in 2010 compared to 1992 and 2002.

BACKGROUND AND AIM: Significant tumour progression was observed during waiting time for treatment of head and neck cancer. To reduce waiting times, a Danish national policy of fast track accelerated clinical pathways was introduced in 2007. This study describes changes in waiting time and the potential influence of fast track by comparing waiting times in 2010 to 2002 and 1992. METHODS: Charts of all new patients diagnosed with squamous cell carcinoma of the oral cavity, pharynx and larynx at the five Danish head and neck oncology centres from January to April 2010 (n=253) were reviewed and compared to similar data from 2002 (n=211) and 1992 (n=168). RESULTS: The median time to diagnosis was 13 days (2010) versus 17 days (2002; p<0.001) and 20 days (1992; p<0.001). Median days from diagnosis to treatment start were 25 (2010) versus 47 (2002; p<0.001) and 31 (1992; p<0.001). Total pre-treatment time was median 41 days in 2010 versus 69 days (2002) (p<0.001) and 50 days (1992; p<0.001). Significantly more diagnostic imaging was done in 2010 compared to 2002 and 1992. When compared to current fast track standards the adherence to diagnosis improved slightly from 47% (1992) to 51% (2002) and 64% (2010); waiting time for radiotherapy was within standards for 7%, 1% and 22% of cases, respectively; waiting time for surgery was within standards for 17%, 22% and 48%, respectively. CONCLUSION: The study showed a significant reduction in delay of diagnosis and treatment of head and neck cancer in 2010, but still less than half of all patients start treatment within the current standards.

National health spending in 2011: overall growth remains low, but some payers and services show signs of acceleration.

In 2011 US health care spending grew 3.9 percent to reach $2.7 trillion, marking the third consecutive year of relatively slow growth. Growth in national health spending closely tracked growth in nominal gross domestic product (GDP) in 2010 and 2011, and health spending as a share of GDP remained stable from 2009 through 2011, at 17.9 percent. Even as growth in spending at the national level has remained stable, personal health care spending growth accelerated in 2011 (from 3.7 percent to 4.1 percent), in part because of faster growth in spending for prescription drugs and physician and clinical services. There were also divergent trends in spending growth in 2011 depending on the payment source: Medicaid spending growth slowed, while growth in Medicare, private health insurance, and out-of-pocket spending accelerated. Overall, there was relatively slow growth in incomes, jobs, and GDP in 2011, which raises questions about whether US health care spending will rebound over the next few years as it typically has after past economic downturns.

Educating for personalized medicine: a perspective from oncology.

The personalized-medicine concept represents the future of oncology medicine. New genomics technologies will characterize patients biologically in ways that will drive more efficient and effective cancer treatment. Yet the introduction of these technologies is disruptive to current practices in clinical oncology, as well as to current regulatory and reimbursement strategies. The efficient introduction of personalized medicine will require education in addition to behavioral and policy changes by the various involved stakeholders.

The microeconomics of personalized medicine: today's challenge and tomorrow's promise.

'Personalized medicine' promises to increase the quality of clinical care and, in some cases, decrease health-care costs. Despite this, only a handful of diagnostic tests have made it to market, with mixed success. Historically, the challenges in this field were scientific. However, as discussed in this article, with the maturation of the '-omics' sciences, it now seems that the major barriers are increasingly related to economics. Overcoming the poor microeconomic alignment of incentives among key stakeholders is therefore crucial to catalysing the further development and adoption of personalized medicine, and we propose several actions that could help achieve this goal.

Assessing patient readiness for the clinical adoption of personalized medicine.

BACKGROUND/AIMS: Although pharmacogenomics-based diagnostics and therapeutics are increasingly being translated into personalized medicine applications, relatively little evidence exists about how novel pharmacogenomics-based technologies will be accepted and adopted by patients. It is important to understand the characteristics of genomic diagnostics and targeted therapeutics that might impact utilization or serve as barriers to adoption of these novel technologies in order to formulate appropriate policies and procedures. The objective of this study was to investigate patients' understanding and knowledge of personalized medicine and the process of decision-making regarding pharmacogenomics testing and targeted therapeutics and to better understand how patients value receiving pharmacogenomics-based care. METHODS: We conducted 4 focus groups with 8-10 individuals in each group with patients recruited from out-patient clinics at The Methodist Hospital in Houston, Tex., USA. RESULTS: The use of genomic diagnostics and targeted therapeutics to facilitate personalized medicine has considerable support from patients. However, our data revealed that participants were concerned with issues surrounding privacy and confidentiality of genetic test results, particularly with respect to access of information by insurers, with potential costs of testing and issues related to accuracy of test results. Questions regarding willingness to pay revealed that patients would be more willing to pay out-of-pocket if the disease associated with pharmacogenomic testing for treatment was perceived to be high risk (e.g., colorectal cancer) versus a chronic condition that was perceived as lower risk (e.g., high cholesterol). CONCLUSION: As the personalized medicine approach is increasingly incorporated into health care, understanding patients' needs and their readiness to adopt these novel technologies will become progressively more important for the development of appropriate health policies.

The disruptive nature of personalized medicine technologies: implications for the health care system.

Genomics technologies, notwithstanding rising complexity and low productivity to date, once translated to clinical care, promise significantly improved outcomes through cost-effective interventions and prevention. But, along the way, every business model and every stakeholder group will be challenged to adapt to the disruptions that will arise as our health care system seeks to embrace those technologies. This paper identifies many of the key issues and stakeholders to be directly effected, including payers, providers, and suppliers. An even greater challenge faces public policy makers if these technologies are to be optimized. Many of these issues are raised as well. Finally, the point is made that the greatest barriers are not necessarily raised by stakeholders but rather arise from the deepening complexity of the science itself, requiring a long-term, large, and consistent research commitment from both the public and private sectors - a commitment made harder by the indisputable need to reform the current health care system.

The current status and future potential of personalized diagnostics: Streamlining a customized process.

Recent genetic discoveries and related developments in genomic techniques have led to the commercialization of novel diagnostic platforms for studying disease or gauging therapeutic outcomes in individual patients. This newly emerging field is called "personalized medicine," and uses the patient's genetic composition to tailor strategies for patient-specific disease detection, treatment, or prevention. Personalized diagnostic tests are used to detect patient-to-patient variations in gene or protein expression levels, which act as indicators for drug treatments or disease prognosis. In turn, medical professionals can better answer questions such as: "Who should be treated with which drug?" and "How should the treatment be administered?" The regulations governing personalized medicine can be complicated because they encompass in vitro diagnostic systems and laboratory tests as well as methods of disease treatment and patient care. Industry, academia, medicine, and the Food and Drug Administration (FDA) are all involved in the cultivation of the field: substantial collaborations between drug developers and regulatory authorities are required to consider and shape emerging regulations as personalized drug strategies mature. Some of the regulatory issues identified by industry and the FDA about personalized medicine and personalized diagnostics will be addressed. In addition, relevant collaborations, advances, and current and draft regulatory guidances will be discussed with respect to the future of personalized medicine.