Office of the Assistant Secretary for Planning and Evaluation

BACKGROUND: The Hospital Readmissions Reduction Program, which is included in the Affordable Care Act (ACA), applies financial penalties to hospitals that have higher-than-expected readmission rates for targeted conditions. Some policy analysts worry that reductions in readmissions are being achieved by keeping returning patients in observation units instead of formally readmitting them to the hospital. We examined the changes in readmission rates and stays in observation units over time for targeted and nontargeted conditions and assessed whether hospitals that had greater increases in observation-service use had greater reductions in readmissions. METHODS: We compared monthly, hospital-level rates of readmission and observation-service use within 30 days after hospital discharge among Medicare elderly beneficiaries from October 2007 through May 2015. We used an interrupted time-series model to determine when trends changed and whether changes differed between targeted and nontargeted conditions. We assessed the correlation between changes in readmission rates and use of observation services after adoption of the ACA in March 2010. RESULTS: We analyzed data from 3387 hospitals. From 2007 to 2015, readmission rates for targeted conditions declined from 21.5% to 17.8%, and rates for nontargeted conditions declined from 15.3% to 13.1%. Shortly after passage of the ACA, the readmission rate declined quickly, especially for targeted conditions, and then continued to fall at a slower rate after October 2012 for both targeted and nontargeted conditions. Stays in observation units for targeted conditions increased from 2.6% in 2007 to 4.7% in 2015, and rates for nontargeted conditions increased from 2.5% to 4.2%. Within hospitals, there was no significant association between changes in observation-unit stays and readmissions after implementation of the ACA. CONCLUSIONS: Readmission trends are consistent with hospitals' responding to incentives to reduce readmissions, including the financial penalties for readmissions under the ACA. We did not find evidence that changes in observation-unit stays accounted for the decrease in readmissions.

IMPORTANCE: The Affordable Care Act (ACA) completed its second open enrollment period in February 2015. Assessing the law's effects has major policy implications. OBJECTIVES: To estimate national changes in self-reported coverage, access to care, and health during the ACA's first 2 open enrollment periods and to assess differences between low-income adults in states that expanded Medicaid and in states that did not expand Medicaid. DESIGN, SETTING, AND PARTICIPANTS: Analysis of the 2012-2015 Gallup-Healthways Well-Being Index, a daily national telephone survey. Using multivariable regression to adjust for pre-ACA trends and sociodemographics, we examined changes in outcomes for the nonelderly US adult population aged 18 through 64 years (n = 507,055) since the first open enrollment period began in October 2013. Linear regressions were used to model each outcome as a function of a linear monthly time trend and quarterly indicators. Then, pre-ACA (January 2012-September 2013) and post-ACA (January 2014-March 2015) changes for adults with incomes below 138% of the poverty level in Medicaid expansion states (n = 48,905 among 28 states and Washington, DC) vs nonexpansion states (n = 37,283 among 22 states) were compared using a differences-in-differences approach. EXPOSURES: Beginning of the ACA's first open enrollment period (October 2013). MAIN OUTCOMES AND MEASURES: Self-reported rates of being uninsured, lacking a personal physician, lacking easy access to medicine, inability to afford needed care, overall health status, and health-related activity limitations. RESULTS: Among the 507,055 adults in this survey, pre-ACA trends were significantly worsening for all outcomes. Compared with the pre-ACA trends, by the first quarter of 2015, the adjusted proportions who were uninsured decreased by 7.9 percentage points (95% CI, -9.1 to -6.7); who lacked a personal physician, -3.5 percentage points (95% CI, -4.8 to -2.2); who lacked easy access to medicine, -2.4 percentage points (95% CI, -3.3 to -1.5); who were unable to afford care, -5.5 percentage points (95% CI, -6.7 to -4.2); who reported fair/poor health, -3.4 percentage points (95% CI, -4.6 to -2.2); and the percentage of days with activities limited by health, -1.7 percentage points (95% CI, -2.4 to -0.9). Coverage changes were largest among minorities; for example, the decrease in the uninsured rate was larger among Latino adults (-11.9 percentage points [95% CI, -15.3 to -8.5]) than white adults (-6.1 percentage points [95% CI, -7.3 to -4.8]). Medicaid expansion was associated with significant reductions among low-income adults in the uninsured rate (differences-in-differences estimate, -5.2 percentage points [95% CI, -7.9 to -2.6]), lacking a personal physician (-1.8 percentage points [95% CI, -3.4 to -0.3]), and difficulty accessing medicine (-2.2 percentage points [95% CI, -3.8 to -0.7]). CONCLUSIONS AND RELEVANCE: The ACA's first 2 open enrollment periods were associated with significantly improved trends in self-reported coverage, access to primary care and medications, affordability, and health. Low-income adults in states that expanded Medicaid reported significant gains in insurance coverage and access compared with adults in states that did not expand Medicaid.

OBJECTIVE: A recent HIV outbreak in a rural network of persons who inject drugs (PWID) underscored the intersection of the expanding epidemics of opioid abuse, unsterile injection drug use (IDU), and associated increases in hepatitis C virus (HCV) infections. We sought to identify US communities potentially vulnerable to rapid spread of HIV, if introduced, and new or continuing high rates of HCV infections among PWID. DESIGN: We conducted a multistep analysis to identify indicator variables highly associated with IDU. We then used these indicator values to calculate vulnerability scores for each county to identify which were most vulnerable. METHODS: We used confirmed cases of acute HCV infection reported to the National Notifiable Disease Surveillance System, 2012-2013, as a proxy outcome for IDU, and 15 county-level indicators available nationally in Poisson regression models to identify indicators associated with higher county acute HCV infection rates. Using these indicators, we calculated composite index scores to rank each county's vulnerability. RESULTS: A parsimonious set of 6 indicators were associated with acute HCV infection rates (proxy for IDU): drug-overdose deaths, prescription opioid sales, per capita income, white, non-Hispanic race/ethnicity, unemployment, and buprenorphine prescribing potential by waiver. Based on these indicators, we identified 220 counties in 26 states within the 95th percentile of most vulnerable. CONCLUSIONS: Our analysis highlights US counties potentially vulnerable to HIV and HCV infections among PWID in the context of the national opioid epidemic. State and local health departments will need to further explore vulnerability and target interventions to prevent transmission.

The "activities of daily living," or ADLs, are the basic tasks of everyday life, such as eating, bathing, dressing, toileting, and transferring. Reported estimates of the size of the elderly population with ADL disabilities differ substantially across national surveys. Differences in which ADL items are being measured and in what constitutes a disability account for much of the variation. Other likely explanations are differences in sample design, sample size, survey methodology, and age structure of the population to which the sample refers. When essentially equivalent ADL measures are compared, estimates for the community-based population vary by up to 3.1 percentage points; and for the institutionalized population, with the exception of toileting, by no more than 3.2 percentage points. As small as these differences are in absolute terms, they can be large in percent differences across surveys. For example, the National Medical Expenditure Survey estimates that there are 60 percent more elderly people with ADL problems than does the Supplement on Aging.

BACKGROUND: The increasing cost of clinical research has significant implications for public health, as it affects drug companies' willingness to undertake clinical trials, which in turn limits patient access to novel treatments. Thus, gaining a better understanding of the key cost drivers of clinical research in the United States is important. PURPOSE: The study which is based on a report prepared by Eastern Research Group, Inc., for the US Department of Health and Human Services, examined different factors, such as therapeutic area, patient recruitment, administrative staff, and clinical procedure expenditures, and their contribution to pharmaceutical clinical trial costs in the United States by clinical trial phase. METHODS: The study used aggregate data from three proprietary databases on clinical trial costs provided by Medidata Solutions. We evaluated per-study costs across therapeutic areas by aggregating detailed (per patient and per site) cost information. We also compared average expenditures on cost drivers with the use of weighted mean and standard deviation statistics. RESULTS: Therapeutic area was an important determinant of clinical trial costs by phase. The average cost of a Phase 1 study conducted at a US site ranged from US$1.4 million (pain and anesthesia) to US$6.6 million (immunomodulation), including estimated site overhead and monitoring costs of the sponsoring organization. A Phase 2 study cost from US$7.0 million (cardiovascular) to US$19.6 million (hematology), whereas a Phase 3 study cost ranged from US$11.5 million (dermatology) to US$52.9 (pain and anesthesia) on average. Across all study phases and excluding estimated site overhead costs and costs for sponsors to monitor the study, the top three cost drivers of clinical trial expenditures were clinical procedure costs (15%-22% of total), administrative staff costs (11%-29% of total), and site monitoring costs (9%-14% of total). LIMITATIONS: The data were from 2004 through 2012 and were not adjusted for inflation. Additionally, the databases used represented a convenience, that is, non-probability, sample and did not allow for statistically valid estimates of cost drivers. Finally, the data were from trials funded by the global pharmaceutical and biotechnology industry only. Hence, our study findings are limited to that segment. CONCLUSION: Therapeutic area being studied as well as number and types of clinical procedures involved were the key drivers of direct costs in Phase 1 through Phase 3 studies. Research shows that strategies exist for reducing the price tag of some of these major direct cost components. Therefore, to increase clinical trial efficiency and reduce costs, gaining a better understanding of the key direct cost drivers is an important step.

The Affordable Care Act enables young adults to remain as dependents on their parents' health insurance until age twenty-six, and recent evidence suggests that as many as three million young adults have gained coverage as a result. However, there has been no evidence yet on the policy's effect on access to care, and questions remain about the coverage impact on important subgroups. Using data from two nationally representative surveys, comparing young adults who gained access to dependent coverage to a control group (adults ages 26-34) who were not affected by the new policy, we found sizable coverage gains for adults ages 19-25. The gains continued to grow throughout 2011 (up 6.7 percentage points from September 2010 to September 2011), with the largest gains seen in unmarried adults, nonstudents, and men. Analysis of the timing of the policy impact suggested that early gains in coverage were greatest for people in worse health. We found strong evidence of increased access to care because of the law, with significant reductions in the number of young adults who delayed getting care and in those who did not receive needed care because of cost.

In September 2002, a technical working group met to resolve previously published inconsistencies across national surveys in trends in activity limitations among the older population. The 12-person panel prepared estimates from five national data sets and investigated methodological sources of the inconsistencies among the population aged 70 and older from the early 1980s to 2001. Although the evidence was mixed for the 1980s and it is difficult to pinpoint when in the 1990s the decline began, during the mid- and late 1990s, the panel found consistent declines on the order of 1%-2.5% per year for two commonly used measures in the disability literature: difficulty with daily activities and help with daily activities. Mixed evidence was found for a third measure: the use of help or equipment with daily activities. The panel also found agreement across surveys that the proportion of older persons who receive help with bathing has declined at the same time as the proportion who use only equipment (but not personal care) to bathe has increased. In comparing findings across surveys, the panel found that the period, definition of disability, treatment of the institutionalized population, and age standardizing of results were important to consider. The implications of the findings for policy, national survey efforts, and further research are discussed.

Achieving nationwide adoption of electronic health records (EHRs) remains an important policy priority. While EHR adoption has increased steadily since 2010, it is unclear how providers that have not yet adopted will fare now that federal incentives have converted to penalties. We used 2008-14 national data, which includes the most recently available, to examine hospital EHR trends. We found large gains in adoption, with 75 percent of US hospitals now having adopted at least a basic EHR system--up from 59 percent in 2013. However, small and rural hospitals continue to lag behind. Among hospitals without a basic EHR system, the function most often not yet adopted (in 61 percent of hospitals) was physician notes. We also saw large increases in the ability to meet core stage 2 meaningful-use criteria (40.5 percent of hospitals, up from 5.8 percent in 2013); much of this progress resulted from increased ability to meet criteria related to exchange of health information with patients and with other physicians during care transitions. Finally, hospitals most often reported up-front and ongoing costs, physician cooperation, and complexity of meeting meaningful-use criteria as challenges. Our findings suggest that nationwide hospital EHR adoption is in reach but will require attention to small and rural hospitals and strategies to address financial challenges, particularly now that penalties for lack of adoption have begun.

The United States is making substantial investments to accelerate the adoption and use of interoperable electronic health record (EHR) systems. Using data from the 2009-13 Electronic Health Records Survey, we found that EHR adoption continues to grow: In 2013, 78 percent of office-based physicians had adopted some type of EHR, and 48 percent had the capabilities required for a basic EHR system. However, we also found persistent gaps in EHR adoption, with physicians in solo practices and non-primary care specialties lagging behind others. Physicians' electronic health information exchange with other providers was limited, with only 14 percent sharing data with providers outside their organization. Finally, we found that 30 percent of physicians routinely used capabilities for secure messaging with patients, and 24 percent routinely provided patients with the ability to view online, download, or transmit their health record. These findings suggest that although EHR adoption continues to grow, policies to support health information exchange and patient engagement will require ongoing attention.

The authors assessed the relationship between having a regular doctor and access to care, as measured by a set of preventive and primary care utilization indicators recommended by the Institute of Medicine. The 1987 National Medical Expenditure Survey was used in the analyses (n = 30,012). The results of the regression analyses suggest that individuals with any type of regular source of care had better access than those without a regular source of care. Persons with a regular doctor had better access to primary care than those with a regular site but no regular doctor. However, the apparent advantage of having a regular doctor over a regular site disappeared when only those individuals reporting a physician's office, clinic, or health maintenance organization as their regular source of care were compared. These results suggest that policies that promote the doctor-patient relationship will increase access, although the gains may be negligible for individuals who use mainstream primary care sites (physician's office, clinic, or health maintenance organization) versus sites such as walk-in clinics or emergency rooms.

BACKGROUND: Schools play an important role as providers of mental health services for adolescents; however, information on the broader picture of utilization of mental health services in educational versus other settings is limited because of the lack of national-level data. METHODS: Using multinomial logistic regression models based on national-level data from the 2012-2015 National Survey on Drug Use and Health, we explore the characteristics of adolescents who received mental health treatment in educational and other settings. In addition, the study examines the reasons for seeking services in various treatment settings. RESULTS: The analysis finds that while the majority of adolescents who access mental health services receive care at noneducational settings, slightly more than one-third of them received services only in an educational setting. Adolescents who had public insurance, were from low-income households, and were from racial/ethnic minority groups were more likely to access services in an educational setting only. Common reasons for accessing services in educational settings included problems with schools, friends, and family members. CONCLUSIONS: Despite increased access to treatment in outpatient settings in the last decade, schools play an important role in providing access to mental health services for disadvantaged populations.

Data are central to research, public health, and in developing health information technology (IT) systems. Nevertheless, access to most data in health care is tightly controlled, which may limit innovation, development, and efficient implementation of new research, products, services, or systems. Using synthetic data is one of the many innovative ways that can allow organizations to share datasets with broader users. However, only a limited set of literature is available that explores its potentials and applications in health care. In this review paper, we examined existing literature to bridge the gap and highlight the utility of synthetic data in health care. We searched PubMed, Scopus, and Google Scholar to identify peer-reviewed articles, conference papers, reports, and thesis/dissertations articles related to the generation and use of synthetic datasets in health care. The review identified seven use cases of synthetic data in health care: a) simulation and prediction research, b) hypothesis, methods, and algorithm testing, c) epidemiology/public health research, d) health IT development, e) education and training, f) public release of datasets, and g) linking data. The review also identified readily and publicly accessible health care datasets, databases, and sandboxes containing synthetic data with varying degrees of utility for research, education, and software development. The review provided evidence that synthetic data are helpful in different aspects of health care and research. While the original real data remains the preferred choice, synthetic data hold possibilities in bridging data access gaps in research and evidence-based policymaking.

When “disability” was added to public health measures, which had traditionally focused on mortality, it had a “Cinderella effect” on mental disorders. These disorders had never been put on public health priority lists. However, when “disability” was entered into the equation, as was the case with the disability adjusted life years (DALYs), mental disorders ranked as high as cardiovascular and respiratory diseases, surpassing all malignancies combined, or HIV 1. Using DALYs, the Global Burden of Disease study thus revealed the true magnitude of the long underestimated impact of mental health problems, due to the disability they produce 2. Disability in mental disorders is a well-known fact for many clinicians, policy makers and researchers, as well as caregivers and persons with mental illness. Yet the form, frequency and outcome of disabilities in mental disorders are not well-defined or studied scientifically. More-over, their use in formulating diagnoses of mental disorders is both unclear and inconsistent. The World Health Organization (WHO) and the American Psychiatric Association (APA) use the construct of disability very differently in their classification systems. Without focused attention on functioning and disabilities, the current revisions of WHO's International Classification of Diseases (ICD) 3 and APA's Diagnostic and Statistical Manual of Mental Disorders (DSM) 4 will perpetuate divergence in diagnosing mental disorders. This would have the potential to con-found international research and clinical care. In this paper, we propose to define disability operationally and separate it from the disease process in the diagnosis of mental disorders in both ICD and DSM systems. Compatibility of the ICD and the DSM was already a stated goal of the DSM-II in 1968. Since then, the two diagnostic classifications have been developed in parallel. In 1980, the DSM-III was a revolutionary development in operationalizing the diagnostic criteria for mental disorders, a quest which had been made by Stengel already in 1959 5 and was then adopted in the production of the DSM-IV and the ICD-10. While the phenomenology of mental disorders was operationally defined in line with expert consensus, the formulation of disability (or “functional impairment” in DSM parlance) was not. It was included into the “clinical significance” criterion of the DSM, leaving it open to judgment by clinicians. As shown in Table 1, the DSM, contrary to the ICD system, makes “clinical significance” an explicit part of the criteria for establishing a diagnosis. Clinical significance has two main components: distress and “functional impairment”. Distress is expressed by the subject or his/her significant others in terms of worry, concern, suffering about the condition. Sometimes it may not be expressed or may be explicitly denied. Functional impairment refers to limitations due to the illness, as people with a disease may not carry out certain functions in their daily lives. We operationally equate the “functional impairment” concept with “disability” in the WHO's International Classification of Functioning, Disability and Health (ICF) 6. The DSM term “functional impairment” is not specifically defined. It is used to mean limitations in the social and occupational spheres of life. The DSM-IV-TR also refers to “other important areas of functioning”, but does not identify them. The ICF does not use the term “functional impairment”. In this classification, the term “functioning” is a neutral one, encompassing all body functions, activities and involvement in life situations. The term “disability” means the decrements to these functions, which are known at the body level as impairments, at the person level as activity limitations, and at the societal level as participation restriction. The DSM's use of “functional impairment” can be taken to mean ICF's “disability” largely, or activity limitations, narrowly. The DSM's social functioning would include ICF's interpersonal interactions and relationships, but may also include some of the items concerning participation in community, social and civic life. The DSM's occupational functioning would include the activities listed under the ICF's categories of work and employment. To avoid a confusion, it is useful to note that the impairment of mental functions in the ICF generally corresponds to what is known as signs and symptoms of mental disorders (e.g., consciousness, orientation, energy, sleep, attention, memory, emotions). There are three major ways in which decrements in functioning are used in the DSM-IV-TR. The first is called “functional impairment”, which is described as dysfunction in social and occupational spheres of life, as noted above. Functional impairment is used as a criterion which must be fulfilled in order to render a diagnosis. Although never stated directly, the functional criterion in the DSM implies that a mental disorder must be associated with either distress or disability. As such, it helps establish the “threshold for the diagnosis of a disorder” 4. No guidance is given as to determining the level of disability that would constitute the contribution to the threshold for a diagnosis. It is left open to the clinical judgment of the user, which defies the basic operational approach of the DSM. The second way functions are used is to determine the level of severity of the diagnosed disorder. The three levels of severity (mild, moderate and severe) include both symptoms and “impairments in social and occupational functioning”. Determining the level of severity is a clinical judgment. For example, the DSM-IV-TR's guidance for the “mild” and “severe” includes either “few” or “many” symptoms above the required number and either “minor” or “marked” impairments in social or occupational functioning. “Moderate” is in between. The criteria for mood disorders are somewhat more explicit. Anchors are indicated for mild and severe disability in major depressive episode. Mild disability is addressed as “mild disability or the capacity to function normally but with substantial and unusual effort” 4. Severe disability is characterized as “clear-cut, observable disability (e.g., inability to work or care for children)” 4. The criteria for other mood disorders note different areas of disability, such as social activities and need for supervision, where the amount of supervision provides an anchor for severity 4. The third way functions are used is to plan treatment, track clinical progress and predict treatment outcome. The Global Assessment of Functioning (GAF) is a 100-point scale used to rate both symptoms (i.e., part of the disease construct) and psychological, social or occupational functioning (i.e., part of the disability construct). Thus, in the construction of the GAF, the constructs of disease and disability are con-founded in each other. This entanglement does not allow a separate operational measurement of disability. In summary, in the DSM system, making a diagnosis (and determining its level of severity) depends on a conjoint assessment of symptoms and functioning. These constructs are never assessed separately. The ICD chapter V keeps the disability construct separate from the diagnosis of mental disorders. Disability is a discrete phenomenon that is evaluated separately in a different classification scheme, the ICF, as a complementing member of the WHO family of classifications. The ICF's information on functioning and disability enriches the diagnostic information in the ICD, providing a broader, more meaningful picture of the patient's health, which can be used for better management decisions. This separate assessment also allows studying the association between the disorder and disability by scientific methods. Nevertheless, difficulties in a person's functioning are occasionally included in the ICD classification of mental disorders. For example, decrements in functioning, such as poor self-care and social performance, are included as part of the description of negative symptoms in residual schizophrenia (F20.5). In this context, it is useful to note that ICD revision efforts will specifically review the diagnostic criteria to cleanly separate disease and disability constructs. The issue of disability is confounded by the definition of severity in mental disorders. Usually there is a positive correlation between the severity of an illness and the consequent disability; hence it is easy to fall in this trap. Unless one takes conceptual safeguards to differentiate severity of a mental disorder from the functional limitations that may result, it is not possible to study the interaction between the two. Theoretically, the severity of an illness is dependent on its development, spread, or the depth of dysfunction it causes in body systems. Disability is an outcome of the underlying disease in a given environment, concerning what people can do in terms of activities. For example, the severity of tuberculosis depends on factors such as the virulence of the bacteria, or the spread of the disease in the body, whereas disability depends on whether the patient with tuberculosis can work, go to school or carry out other daily activities. The severity of a mental disorder is not always clearly and operationally defined in DSM, and is unfortunately confounded with a combination of the symptomatic constellation of the disorder and the limitations in social or occupational functioning. For example, the DSM-IV-TR explicitly states that the level of severity of a major depressive disorder or bipolar I disorder should be coded in the fifth digit. The three levels of severity defined in the DSM-IV-TR, as noted above, are: mild (few, if any, symptoms in excess of those required to make the diagnosis are present, and symptoms result in no more than minor impairment in social or occupational functioning); moderate (symptoms or functional impairment between “mild” and “severe” are present); severe (many symptoms in excess of those required to make the diagnosis, or several symptoms that are particularly severe, are present, or the symptoms result in marked impairment in social or occupational functioning) 4. If one aims to apply a similar disease construct to both mental and physical diseases, other ways of formulating the severity of mental disorder should be explored. For example, the severity of a physical disease or disorder can be conceived in different ways: a) various thresholds on an indicator (such as mild, moderate or severe hypertension in terms of blood pressure levels); b) staging of the progress or dissemination of a disease (e.g., stage 1, 2, 3 of syphilis; classification of tumors according to the stage of their development); c) degree of complications (such as in latent, manifest and complicated diabetes mellitus). There may be other or mixed models of severity of a disease. However, functional consequences in terms of what a patient has difficulty to do is a different construct from the severity of the disease, and has to be evaluated separately. Severe forms of diseases usually cause more disability; however, disability emerges from an interaction between the person and the environment. Depending on the context, there may be no disability in a severe disease or some disability in very mild forms of mental disorders. To address this confounding relationship, distinct constructs of disorder/disease and disability have to be operationalized 7,9. Currently two basic problems exist that require a solution: a) severity of symptoms (to assess the severity of many symptoms, the DSM calls for rating of functioning in a combined fashion; the ICD system, instead, does not call for ratings of functioning or disability to assess symptomatic severity); b) clinical significance of syndromes (the DSM calls for associated disability − functional impairment − as a requirement for the diagnosis of mental disorder; the ICD does not have this criterion and leaves this area to the ICF, which describes how functioning can be rated using qualifiers that connote the degree of the problem). The DSM-V and the ICD-11 can be made compatible by allowing a separate operational assessment of disability through the DSM's GAF scale and the ICF-linked assessment instruments, such as the WHO Disability Assessment Schedule (WHODAS, 10). The key question is how to operationalize the ICF constructs in a succinct and clinically relevant way. Several assessment tools based on this classification system may help in identifying key areas of functioning. For example, the ICF Checklist provides the basis for a clinical assessment tool, covering the areas of cognition, communication, mobility, self-care, interpersonal relations, domestic and occupational life activities, and community, social and civic life. When these areas are coded as “present”, decrements can either be rated as mild, moderate, severe, or the total number of items can be summed, but the scale does not necessarily yield a cardinal measure of disability. Standardized metric information is needed, and can be gleaned from research conducted with the WHODAS on the population of individuals with mental disorders. A review of clinical experience and research using the ICF Checklist can support a revised set of items and assessment methodology. As evident in Table 2, the ICF Checklist includes all domains of function/disability of the GAF scale. In the ICF Checklist, the nominal code is 0 for no difficulty, 1 for mild difficulty, 2 for moderate, 3 for severe, and 4 for complete. On a scale of 0 to 100, 0 to 4 percent is interpreted as no problem, 5 to 24 percent is a mild problem, 25 to 49 percent is a moderate problem, 50 to 95 percent is severe, and 95 to 100 is total or complete problem. The scale is calibrated in the opposite direction of the GAF scale, in which 91 to 100 is superior functioning. The GAF scale decile system is not translated into levels of severity such as mild, moderate or severe. The only WHODAS domain that is not consistent with the activities section of ICF Checklist queries about cognition. The items within this WHODAS domain would be assessed by the clinician as part of identifying symptoms of the disorders. We need an internationally agreed conceptualization between ICD and DSM in terms of better operationalization of disease and disability components. This can be achieved by starting to use ICF domains in an operational way. In this way, thresholds for each domain of functioning could be better defined. No functioning or disability should appear as part of the threshold of the diagnosis in either system. A separate rating of the disorder severity (i.e., mild, moderate, or severe), after a diagnosis has been made, would rely on an assessment of the development of the disease, its spread, continuity or any measure independent of disability parameters, so as to avoid co-linearity. To put mental health in parity with the rest of health care, and integrate mental health to general health information systems, the classifications in mental health cannot afford to continue separate lines of development and should include common models and elements, including common terminology and ontology about signs, symptoms, functioning and other entities. This will create better scientific research which will lead to better assessment of outcomes and comparisons of effectiveness of health interventions. The views expressed in this paper are personal opinions of the authors and do not necessarily represent the official views of their institutions or organizations, including the WHO and the US government. The authors gratefully acknowledge the helpful discussions and contributions from Angelo Barbato, Tae-Yeon Hwang, Aleksander Janca, Marianne Kastrup, Venos Mavreas, William Narrow, Martti Virtanen, Nenad Kostanjsek, Somnath Chatterji and Robert Jakob.

PROBLEM/CONDITION: Drug overdoses are a leading cause of injury death in the United States, resulting in approximately 52,000 deaths in 2015. Understanding differences in illicit drug use, illicit drug use disorders, and overall drug overdose deaths in metropolitan and nonmetropolitan areas is important for informing public health programs, interventions, and policies. REPORTING PERIOD: Illicit drug use and drug use disorders during 2003-2014, and drug overdose deaths during 1999-2015. DESCRIPTION OF DATA: The National Survey of Drug Use and Health (NSDUH) collects information through face-to-face household interviews about the use of illicit drugs, alcohol, and tobacco among the U.S. noninstitutionalized civilian population aged ≥12 years. Respondents include residents of households and noninstitutional group quarters (e.g., shelters, rooming houses, dormitories, migratory workers' camps, and halfway houses) and civilians living on military bases. NSDUH variables include sex, age, race/ethnicity, residence (metropolitan/nonmetropolitan), annual household income, self-reported drug use, and drug use disorders. National Vital Statistics System Mortality (NVSS-M) data for U.S. residents include information from death certificates filed in the 50 states and the District of Columbia. Cases were selected with an underlying cause of death based on the ICD-10 codes for drug overdoses (X40-X44, X60-X64, X85, and Y10-Y14). NVSS-M variables include decedent characteristics (sex, age, and race/ethnicity) and information on intent (unintentional, suicide, homicide, or undetermined), location of death (medical facility, in a home, or other [including nursing homes, hospices, unknown, and other locations]) and county of residence (metropolitan/nonmetropolitan). Metropolitan/nonmetropolitan status is assigned independently in each data system. NSDUH uses a three-category system: Core Based Statistical Area (CBSA) of ≥1 million persons; CBSA of <1 million persons; and not a CBSA, which for simplicity were labeled large metropolitan, small metropolitan, and nonmetropolitan. Deaths from NVSS-M are categorized by the county of residence of the decedent using CDC's National Center for Health Statistics 2013 Urban-Rural Classification Scheme, collapsed into two categories (metropolitan and nonmetropolitan). RESULTS: Although both metropolitan and nonmetropolitan areas experienced significant increases from 2003-2005 to 2012-2014 in self-reported past-month use of illicit drugs, the prevalence was highest for the large metropolitan areas compared with small metropolitan or nonmetropolitan areas throughout the study period. Notably, past-month use of illicit drugs declined over the study period for the youngest respondents (aged 12-17 years). The prevalence of past-year illicit drug use disorders among persons using illicit drugs in the past year varied by metropolitan/nonmetropolitan status and changed over time. Across both metropolitan and nonmetropolitan areas, the prevalence of past-year illicit drug use disorders declined during 2003-2014. In 2015, approximately six times as many drug overdose deaths occurred in metropolitan areas than occurred in nonmetropolitan areas (metropolitan: 45,059; nonmetropolitan: 7,345). Drug overdose death rates (per 100,000 population) for metropolitan areas were higher than in nonmetropolitan areas in 1999 (6.4 versus 4.0), however, the rates converged in 2004, and by 2015, the nonmetropolitan rate (17.0) was slightly higher than the metropolitan rate (16.2). INTERPRETATION: Drug use and subsequent overdoses continue to be a critical and complicated public health challenge across metropolitan/nonmetropolitan areas. The decline in illicit drug use by youth and the lower prevalence of illicit drug use disorders in rural areas during 2012-2014 are encouraging signs. However, the increasing rate of drug overdose deaths in rural areas, which surpassed rates in urban areas, is cause for concern. PUBLIC HEALTH ACTIONS: Understanding the differences between metropolitan and nonmetropolitan areas in drug use, drug use disorders, and drug overdose deaths can help public health professionals to identify, monitor, and prioritize responses. Consideration of where persons live and where they die from overdose could enhance specific overdose prevention interventions, such as training on naloxone administration or rescue breathing. Educating prescribers on CDC's guideline for prescribing opioids for chronic pain (Dowell D, Haegerich TM, Chou R. CDC guideline for prescribing opioids for chronic pain-United States, 2016. MMWR Recomm Rep 2016;66[No. RR-1]) and facilitating better access to medication-assisted treatment with methadone, buprenorphine, or naltrexone could benefit communities with high opioid use disorder rates.

Telehealth services have the potential to improve access to care, especially in rural or urban areas with scarce health care resources. Despite the potential benefits, telehealth has not been fully adopted by health centers. This study examined factors associated with and barriers to telehealth use by federally funded health centers. We analyzed data for 2016 from the Uniform Data System using a mixed-methods approach. Our findings suggest that rural location, operational factors, patient demographic characteristics, and reimbursement policies influence health centers' decisions about using telehealth. Cost, reimbursement, and technical issues were described as major barriers. Medicaid reimbursement policies promoting live video and store-and-forward services were associated with a greater likelihood of telehealth adoption. Many health centers were implementing telehealth or exploring its use. Our findings identified areas that policy makers can address to achieve greater telehealth adoption by health centers.

In successfully reducing healthcare expenditures, patient goals must be met and savings differentiated from cost shifting. Although the Department of Veterans Affairs (VA) Home Based Primary Care (HBPC) program for chronically ill individuals has resulted in cost reduction for the VA, it is unknown whether cost reduction results from restricting services or shifting costs to Medicare and whether HBPC meets patient goals. Cost projection using a hierarchical condition category (HCC) model adapted to the VA was used to determine VA plus Medicare projected costs for 9,425 newly enrolled HBPC recipients. Projected annual costs were compared with observed annualized costs before and during HBPC. To assess patient perspectives of care, 31 veterans and caregivers were interviewed from three representative programs. During HBPC, Medicare costs were 10.8% lower than projected, VA plus Medicare costs were 11.7% lower than projected, and combined hospitalizations were 25.5% lower than during the period without HBPC. Patients reported high satisfaction with HBPC team access, education, and continuity of care, which they felt contributed to fewer exacerbations, emergency visits, and hospitalizations. HBPC improves access while reducing hospitalizations and total cost. Medicare is currently testing the HBPC approach through the Independence at Home demonstration.

Building on prior work on rejection sensitivity, we propose a social-cognitive model of gender-based rejection sensitivity (Gender RS) to account for individual differences in how women perceive and cope with gender-based evaluative threats in competitive, historically male institutions. Study 1 develops a measure of Gender RS, defined as anxious expectations of gender-based rejection. Studies 2-5 support the central predictions of the model: Gender RS is associated with increased perceptions of gender-based threats and increased coping by self-silencing--responses that reinforce feelings of alienation and diminished motivation. Study 2 shows that Gender RS is distinct from overall sensitivity to rejection or perceiving the world through the lens of gender. Study 3 shows that Gender RS becomes activated specifically when gender-based rejection is a plausible explanation for negative outcomes. Study 4 provides experimental evidence that Gender RS predicts lower academic self-confidence, greater expectations of bias, and avoidance of opportunities for further help from a weakness-focused expert evaluator. Study 5 tests the Gender RS model in situ, using daily diaries to track women's experiences during the first weeks in a highly competitive law school. Implications for women's coping with the subtle nature of contemporary sexism are discussed as well as the importance of institution-level checks to prevent the costs of gender-based rejection.

Importance: Understanding the cost of drug development can help inform the development of policies to reduce costs, encourage innovation, and improve patient access to drugs. Objective: To estimate the cost of drug development by therapeutic class and trends in pharmaceutical research and development (R&D) intensity over time. Design, Setting, and Participants: In this economic evaluation study, an analytical model of drug development constructed using public and proprietary sources that collectively cover data from 2000 to 2018 was used to estimate the cost of bringing a drug to market, overall and for specific therapeutic classes. The analysis for the study was completed in October 2020. Main Outcomes and Measures: Three measures of development cost from nonclinical through postmarketing stages were estimated: mean out-of-pocket cost or cash outlay, mean expected cost, and mean expected capitalized cost. Pharmaceutical R&D intensity, defined as the ratio of R&D spending to total sales, from 2008 to 2019, based on the time frame for available data, was also analyzed. Results: The estimated mean cost of developing a new drug was approximately $172.7 million (2018 dollars) (range, $72.5 million for genitourinary to $297.2 million for pain and anesthesia), inclusive of postmarketing studies. The cost increased to $515.8 million when cost of failures was included. When the costs of failures and capital were included, the mean expected capitalized cost of drug development increased to $879.3 million (range, $378.7 million for anti-infectives to $1756.2 million for pain and anesthesia); results varied widely by therapeutic class. The pharmaceutical industry as a whole experienced a decline of 15.6% in sales but increased R&D intensity from 11.9% to 17.7% from 2008 to 2019. By contrast, R&D intensity of large pharmaceutical companies increased from 16.6% to 19.3%, whereas sales increased by 10.0% (from $380.0 to $418.0 billion) over the same 2008 to 2019 period, even though the cost of drug development remained relatively stable or may have even decreased. Conclusions and Relevance: In this economic evaluation of new drug development costs, even though the cost of drug development appears to have remained stable, R&D intensity of large pharmaceutical companies remained relatively unchanged, despite substantial growth in revenues during this period. These findings can inform the design of drug-related policies and their potential impacts on innovation and competition.

The Department of Health and Human Services (HHS) recently unveiled the most comprehensive federal commitment yet to reducing racial and ethnic health disparities. The 2011 HHS Action Plan to Reduce Racial and Ethnic Health Disparities not only responds to advice previously offered by stakeholders around the nation, but it also capitalizes on new and unprecedented opportunities in the Affordable Care Act of 2010 to benefit diverse communities. The Action Plan advances five major goals: transforming health care; strengthening the infrastructure and workforce of the nation's health and human services; advancing Americans' health and well-being; promoting scientific knowledge and innovation; and upholding the accountability of HHS for making demonstrable progress. By mobilizing HHS around these goals, the Action Plan moves the country closer to realizing the vision of a nation free of disparities in health and health care.

Problem/ConditionDrug overdoses are a leading cause of injury death in the United States, resulting in approximately 52,000 deaths in 2015. Understanding differences in illicit drug use, illicit drug use disorders, and overall drug overdose deaths in metropolitan and nonmetropolitan areas is important for informing public health programs, interventions, and policies.Reporting PeriodIllicit drug use and drug use disorders during 2003–2014, and drug overdose deaths during 1999–2015.Description of DataThe National Survey of Drug Use and Health (NSDUH) collects information through face-to-face household interviews about the use of illicit drugs, alcohol, and tobacco among the U.S. noninstitutionalized civilian population aged ≥12 years. Respondents include residents of households and noninstitutional group quarters (e.g., shelters, rooming houses, dormitories, migratory workers’ camps, and halfway houses) and civilians living on military bases. NSDUH variables include sex, age, race/ethnicity, residence (metropolitan/nonmetropolitan), annual household income, self-reported drug use, and drug use disorders.National Vital Statistics System Mortality (NVSS-M) data for U.S. residents include information from death certificates filed in the 50 states and the District of Columbia. Cases were selected with an underlying cause of death based on the ICD-10 codes for drug overdoses (X40–X44, X60–X64, X85, and Y10–Y14). NVSS-M variables include decedent characteristics (sex, age, and race/ethnicity) and information on intent (unintentional, suicide, homicide, or undetermined), location of death (medical facility, in a home, or other [including nursing homes, hospices, unknown, and other locations]) and county of residence (metropolitan/nonmetropolitan).Metropolitan/nonmetropolitan status is assigned independently in each data system. NSDUH uses a three-category system: Core Based Statistical Area (CBSA) of ≥1 million persons; CBSA of <1 million persons; and not a CBSA, which for simplicity were labeled large metropolitan, small metropolitan, and nonmetropolitan. Deaths from NVSS-M are categorized by the county of residence of the decedent using CDC’s National Center for Health Statistics 2013 Urban-Rural Classification Scheme, collapsed into two categories (metropolitan and nonmetropolitan).ResultsAlthough both metropolitan and nonmetropolitan areas experienced significant increases from 2003–2005 to 2012–2014 in self-reported past-month use of illicit drugs, the prevalence was highest for the large metropolitan areas compared with small metropolitan or nonmetropolitan areas throughout the study period. Notably, past-month use of illicit drugs declined over the study period for the youngest respondents (aged 12–17 years). The prevalence of past-year illicit drug use disorders among persons using illicit drugs in the past year varied by metropolitan/nonmetropolitan status and changed over time. Across both metropolitan and nonmetropolitan areas, the prevalence of past-year illicit drug use disorders declined during 2003–2014. In 2015, approximately six times as many drug overdose deaths occurred in metropolitan areas than occurred in nonmetropolitan areas (metropolitan: 45,059; nonmetropolitan: 7,345). Drug overdose death rates (per 100,000 population) for metropolitan areas were higher than in nonmetropolitan areas in 1999 (6.4 versus 4.0), however, the rates converged in 2004, and by 2015, the nonmetropolitan rate (17.0) was slightly higher than the metropolitan rate (16.2).InterpretationDrug use and subsequent overdoses continue to be a critical and complicated public health challenge across metropolitan/nonmetropolitan areas. The decline in illicit drug use by youth and the lower prevalence of illicit drug use disorders in rural areas during 2012–2014 are encouraging signs. However, the increasing rate of drug overdose deaths in rural areas, which surpassed rates in urban areas, is cause for concern.Public Health ActionsUnderstanding the differences between metropolitan and nonmetropolitan areas in drug use, drug use disorders, and drug overdose deaths can help public health professionals to identify, monitor, and prioritize responses. Consideration of where persons live and where they die from overdose could enhance specific overdose prevention interventions, such as training on naloxone administration or rescue breathing. Educating prescribers on CDC’s guideline for prescribing opioids for chronic pain (Dowell D, Haegerich TM, Chou R. CDC guideline for prescribing opioids for chronic pain—United States, 2016. MMWR Recomm Rep 2016;66[No. RR-1]) and facilitating better access to medication-assisted treatment with methadone, buprenorphine, or naltrexone could benefit communities with high opioid use disorder rates. Drug overdoses are a leading cause of injury death in the United States, resulting in approximately 52,000 deaths in 2015. Understanding differences in illicit drug use, illicit drug use disorders, and overall drug overdose deaths in metropolitan and nonmetropolitan areas is important for informing public health programs, interventions, and policies. Illicit drug use and drug use disorders during 2003–2014, and drug overdose deaths during 1999–2015. The National Survey of Drug Use and Health (NSDUH) collects information through face-to-face household interviews about the use of illicit drugs, alcohol, and tobacco among the U.S. noninstitutionalized civilian population aged ≥12 years. Respondents include residents of households and noninstitutional group quarters (e.g., shelters, rooming houses, dormitories, migratory workers’ camps, and halfway houses) and civilians living on military bases. NSDUH variables include sex, age, race/ethnicity, residence (metropolitan/nonmetropolitan), annual household income, self-reported drug use, and drug use disorders. National Vital Statistics System Mortality (NVSS-M) data for U.S. residents include information from death certificates filed in the 50 states and the District of Columbia. Cases were selected with an underlying cause of death based on the ICD-10 codes for drug overdoses (X40–X44, X60–X64, X85, and Y10–Y14). NVSS-M variables include decedent characteristics (sex, age, and race/ethnicity) and information on intent (unintentional, suicide, homicide, or undetermined), location of death (medical facility, in a home, or other [including nursing homes, hospices, unknown, and other locations]) and county of residence (metropolitan/nonmetropolitan). Metropolitan/nonmetropolitan status is assigned independently in each data system. NSDUH uses a three-category system: Core Based Statistical Area (CBSA) of ≥1 million persons; CBSA of <1 million persons; and not a CBSA, which for simplicity were labeled large metropolitan, small metropolitan, and nonmetropolitan. Deaths from NVSS-M are categorized by the county of residence of the decedent using CDC’s National Center for Health Statistics 2013 Urban-Rural Classification Scheme, collapsed into two categories (metropolitan and nonmetropolitan). Although both metropolitan and nonmetropolitan areas experienced significant increases from 2003–2005 to 2012–2014 in self-reported past-month use of illicit drugs, the prevalence was highest for the large metropolitan areas compared with small metropolitan or nonmetropolitan areas throughout the study period. Notably, past-month use of illicit drugs declined over the study period for the youngest respondents (aged 12–17 years). The prevalence of past-year illicit drug use disorders among persons using illicit drugs in the past year varied by metropolitan/nonmetropolitan status and changed over time. Across both metropolitan and nonmetropolitan areas, the prevalence of past-year illicit drug use disorders declined during 2003–2014. In 2015, approximately six times as many drug overdose deaths occurred in metropolitan areas than occurred in nonmetropolitan areas (metropolitan: 45,059; nonmetropolitan: 7,345). Drug overdose death rates (per 100,000 population) for metropolitan areas were higher than in nonmetropolitan areas in 1999 (6.4 versus 4.0), however, the rates converged in 2004, and by 2015, the nonmetropolitan rate (17.0) was slightly higher than the metropolitan rate (16.2). Drug use and subsequent overdoses continue to be a critical and complicated public health challenge across metropolitan/nonmetropolitan areas. The decline in illicit drug use by youth and the lower prevalence of illicit drug use disorders in rural areas during 2012–2014 are encouraging signs. However, the increasing rate of drug overdose deaths in rural areas, which surpassed rates in urban areas, is cause for concern. Understanding the differences between metropolitan and nonmetropolitan areas in drug use, drug use disorders, and drug overdose deaths can help public health professionals to identify, monitor, and prioritize responses. Consideration of where persons live and where they die from overdose could enhance specific overdose prevention interventions, such as training on naloxone administration or rescue breathing. Educating prescribers on CDC’s guideline for prescribing opioids for chronic pain (Dowell D, Haegerich TM, Chou R. CDC guideline for prescribing opioids for chronic pain—United States, 2016. MMWR Recomm Rep 2016;66[No. RR-1]) and facilitating better access to medication-assisted treatment with methadone, buprenorphine, or naltrexone could benefit communities with high opioid use disorder rates.