Characteristics of pivotal trials and FDA review of innovative devices.

When patients lack sufficient treatment options for serious medical conditions, they rely on the prompt approval and development of new therapeutic alternatives, such as medical devices. Understanding the development of innovative medical devices, including the characteristics of premarket clinical trials and length of Food and Drug Administration (FDA) review, can help identify ways to expedite patient access to novel technologies and inform recent efforts by FDA to more quickly get these products to patients and physicians. We analyzed publicly available information on clinical trials and premarket FDA review for innovative medical devices that fill an unmet medical need. In this first-of-its-kind study focusing on these products, we extracted data on the length of the pivotal trials, primary study endpoint and FDA review; number of patients enrolled in trials; and in what country the device was available first. We identified 27 approved priority review devices from January 2006 through August 2013. The median duration of pivotal clinical trials was 3 years, ranging from 3 months to approximately 7 years. Trials had a median primary outcome measure evaluation time of one year and a median enrollment of 297 patients. The median FDA review time was 1 year and 3 months. Most priority review devices were available abroad before they were approved in the United States. Our study indicates that addressing the length of clinical studies--and contributing factors, such as primary outcome measures and enrollment--could expedite patient access to innovative medical devices. FDA, manufacturers, Congress and other stakeholders should identify the contributing factors to the length of clinical development, and implement appropriate reforms to address those issues.

Assessing the safety and effectiveness of devices after US Food and Drug Administration approval: FDA-mandated postapproval studies.

IMPORTANCE: Postmarketing surveillance is critical to evaluating the safety and effectiveness of medical devices. The US Food and Drug Administration (FDA) may order the manufacturer of a high-risk device to conduct postmarketing surveillance studies, known as postapproval studies (PASs), at the time of approval. OBJECTIVES: To understand the characteristics of PASs ordered in recent years and inform discussions about the direction of the PASs program. DESIGN: Descriptive study of the PASs ordered for medical devices using the FDA's PASs website, the Premarket Approval database, and supplemental information provided by the FDA. MAIN OUTCOMES AND MEASURES: The proportion of medical devices that received a PAS order and study characteristics. RESULTS: Between January 1, 2005, and December 31, 2011, the FDA ordered 223 studies of 158 medical devices, including studies for 93 (48%) new high-risk devices approved during this period. The median required sample size for a study was 350 patients (interquartile range, 160-1500). If the protocol of a study was not in place at the time the device was approved, which occurred frequently, a median of 180 days elapsed until the protocol was agreed on. The FDA has never issued a warning letter or penalty owing to study delays, inadequate progress, or any other issue related to a PAS. Of the approved protocols, 41 (19%) were subsequently revised, including 29 (21%) protocols in place by application approval. Some studies generated significant clinical findings. The most common effect of a PAS finding after study completion was that the FDA requested a labeling change for 31 studies (53%). CONCLUSIONS AND RELEVANCE: Postapproval studies have the potential to provide additional information to better understand medical device performance. However, small sample sizes, delays in reaching protocol agreement, and lack of availability of findings may hinder their ability to be clinically useful. Owing to the lack of information on the effect of studies, it is unclear whether the program achieves its aims. Improved completion and accessibility of PASs could help answer important questions of safety and effectiveness about medical devices. To better understand the real-world performance of these products, they should be better integrated with other sources of information about device performance.

Who is at risk for special health care needs: findings from the National Survey of Children's Health.

OBJECTIVE: A conceptual model of risk factors for special health care needs in childhood was presented previously. This article uses that conceptual model to identify candidate variables for an exploratory empirical examination of the effects of factors that may increase or decrease the risk of developing a special health care need. METHODS: The National Survey of Children's Health was used for our analysis (N = 102 353). We used multilevel and multivariate analysis methods. We examined risk factors for special health care needs generally and for specific physical, developmental, behavioral, and emotional conditions cooccurring with special health care needs. Risk factors were grouped into 6 major domains, namely, predisposing characteristics, genetic endowment, physical environment, social environment, health-influencing behavior, and health care system characteristics. We examined preschool-aged and school-aged children separately. RESULTS: Significant associations were found in 5 of 6 domains studied (no variables in the health care systems characteristics were significant). Individual variables found to decrease or to increase significantly the odds of experiencing special health care needs were expressed at the child level (eg, age and gender), family level (eg, family structure and family conflict), and neighborhood level (eg, perception of supportiveness of the neighborhood). CONCLUSIONS: This analysis is the first to consider empirically a range of risk factors for special health care needs, using a population health model. Although provisional, the results of our analysis can help us to begin thinking about which characteristics of the child, family, and community are worthy of further exploration. Some of the variables we found to be significantly associated with special health care needs, such as age and ethnicity, are immutable. However, we found a number of significant correlates (ie, possible risk factors) that may be amenable to public health interventions, including breastfeeding practices, exposure to secondhand smoke, family closeness, and neighborhood cohesion.

Healthy Young Adults: description and use of an innovative health insurance program.

PURPOSE: To evaluate enrollment patterns, healthcare use, and factors associated with enrollment in San Francisco's Healthy Young Adults (HYA) program, a comprehensive public health insurance program for young adults who age out of public insurance. METHODS: In a group of young adults who were all eligible for HYA, we compared demographic characteristics by enrollment status. Demographic information and use patterns were obtained for individuals who were continuously enrolled in HYA for 12 months. RESULTS: Nearly one quarter of eligible young adults enrolled in the program. Those who enrolled were more likely to have used vision or pharmacy services in the 2 years prior to enrollment, to be Asian, and to be from Chinese-speaking families (p < .001) than those who did not enroll. The large majority (80%) of young adults who enrolled in HYA had a primary care or dental visit during the 12-month study period. More than one third (40%) of enrollees used the prescription benefit, while much smaller percentages used radiology services, the emergency department or inpatient facilities. CONCLUSIONS: Young adults in the City and County of San Francisco who enrolled in HYA had high use rates. Ethnic disparities in enrollment indicate the need for targeted outreach. Adverse selection into the program, although present, was minimal. Local innovations are one way to address the problem of uninsurance in the United States, and HYA could be replicated elsewhere.

The Yes We Can Urban Asthma Partnership: a medical/social model for childhood asthma management.

Pediatric asthma programs have struggled to integrate children's medical and social needs. We developed and piloted an integrated team model for asthma care for low-income children through the Yes We Can Urban Asthma Partnership. Program evaluation demonstrated increases in prescribing controller medications (p <0.05), use of action plans (p<0.001), and the use of mattress covers (p<0.001); and decrease in asthma symptoms (p<0.01). Additional changes occurred within the local system of asthma care to support ongoing efforts to improve asthma management. We conclude that pediatric asthma programs can effectively target the social and medical needs of children in a sustainable manner.

Children at risk for special health care needs.

OBJECTIVE: Despite the widespread use of the Maternal and Child Health Bureau definition of children with special health care needs, no published studies have considered the "at-risk" component of the definition. The purpose of this article is to present a conceptual model of risk for special health care needs. METHODOLOGY: The conceptual model presented here was developed based on a comprehensive review of the literature on the determinants of population health and the etiologic literature for selected representative childhood chronic conditions. RESULTS: Our conceptual model is built on 5 key pillars derived from the literature. First, determinants of health have been demonstrated to include genetic endowment, the physical and social environment, health-related behaviors, and the health care system. Second, the model recognizes that the relative importance of each of these domains in contributing to the presence of a special health care need is likely to vary across the major chronic conditions experienced by children. Third, these domains can be conceptualized as acting at the child, family, community, or societal level. Fourth, the model recognizes the presence of a complex interplay of causal factors influencing the development of chronic conditions and associated special health care needs. Fifth, the model incorporates a temporal aspect to the development of special health care needs. CONCLUSIONS: The conceptual model presented here represents a starting point for thinking about the risk factors that influence the occurrence and severity of a special health care need. The model incorporates many of the important breakthroughs by social epidemiologists over the past 25 years by including a broad range of genetic, social, and environmental risk factors; multiple pathways by which they operate; a time dimension; the notion of differential susceptibility and resilience; and a multilevel approach to considering risk. Nevertheless, we recognize that the conceptual model represents an oversimplification of reality. The study of risk factors for special health care needs remains largely in its infancy and is ripe for additional development.