John H. Holmes

Factors associated with smoking in low-income pregnant women: relationship to birth weight, stressful life events, social support, health behaviors and mental distress

Since low-income women are at increased risk of having low birth weight infants, factors associated with birth weight among such groups have special relevance. Cigarette-smoking has emerged as an important predictor of low birth weight due to intrauterine growth retardation and pre-term delivery. After confirming the relation of smoking with birth weight, we examined the association of smoking with sociodemographic factors, attitudes towards pregnancy, health behaviors, stressful life events, social support, and symptoms of mental distress in a cohort of 458 Central Harlem women. We found that social support, stress and mental health were associated with smoking behavior but not directly with birth weight. These findings suggest that programs designed to modify health behaviors such as smoking during pregnancy must also take into account such characteristics of the women and their environments which may make behavioral change difficult. Moreover, programs aimed at fostering better health behaviors to improve pregnancy outcome may have to extend beyond the current pregnancy, as indicated by an association between prior adverse pregnancy outcome and smoking in the current pregnancy.

Distributed health data networks: a practical and preferred approach to multi-institutional evaluations of comparative effectiveness, safety, and quality of care.

Background:Comparative effectiveness research, medical product safety evaluation, and quality measurement will require the ability to use electronic health data held by multiple organizations. There is no consensus about whether to create regional or national combined (eg, “all payer”) databases for these purposes, or distributed data networks that leave most Protected Health Information and proprietary data in the possession of the original data holders. Objectives:Demonstrate functions of a distributed research network that supports research needs and also address data holders concerns about participation. Key design functions included strong local control of data uses and a centralized web-based querying interface. Research Design:We implemented a pilot distributed research network and evaluated the design considerations, utility for research, and the acceptability to data holders of methods for menu-driven querying. We developed and tested a central, web-based interface with supporting network software. Specific functions assessed include query formation and distribution, query execution and review, and aggregation of results. Results:This pilot successfully evaluated temporal trends in medication use and diagnoses at 5 separate sites, demonstrating some of the possibilities of using a distributed research network. The pilot demonstrated the potential utility of the design, which addressed the major concerns of both users and data holders. No serious obstacles were identified that would prevent development of a fully functional, scalable network. Conclusions:Distributed networks are capable of addressing nearly all anticipated uses of routinely collected electronic healthcare data. Distributed networks would obviate the need for centralized databases, thus avoiding numerous obstacles.

Design of a national distributed health data network.

Key Summary Points: Attributes of a National Distributed Health Data Network Supports both observational and intervention studies. Local data holder control over access and uses of data. Mitigates need to share or exchange protected health information. Singular, multipurpose, multi-institutional infrastructure. A distributed health data network is a system that allows secure remote analysis of separate data sets, each derived from a different medical organizations or health plans records. Such networks allow data holders to retain physical control over use of their data, thereby avoiding many obstacles related to confidentiality, regulation, and proprietary interests. They can be used for observational studies, particularly public health surveillance, and can also provide baseline and follow-up data to support clinical trials, including those that use cluster randomization. In addition, a network can monitor use, adoption, and diffusion of new technologies and clinical evidence. Such networks are critical elements of the learning health care system recommended by the Institute of Medicine (1), which supports the use of routinely collected health care data to improve our understanding of the comparative benefits and harms of medical technologies. The United States will soon be able to analyze data from millions of individuals. Congress has mandated that the U.S. Food and Drug Administration develop a postmarket risk identification and analysis system that covers 100 million persons (2). In addition, the expansion of comparative effectiveness research envisioned by Congress requires access to health care information for large, diverse populations in real-world settings (3). Large, centralized data repositories could support these functions, but we and others (4, 5) believe that a distributed health data network has many practical advantages. First, a distributed network allows data holders to retain physical and logical control of their data. Second, it mitigates many security, proprietary, legal, and privacy concerns, including those regulated by the Privacy and Security Rules of the Health Insurance Portability and Accountability Act (6). Third, it eliminates the need to create, maintain, and secure access to central data repositories. Fourth, it minimizes the need to disclose protected health information outside the data-owning entity. Finally, a distributed network allows data holders to assess, track, and authorize requests for all data uses. Several public agencies have supported the development of single-purpose distributed data networks, either directly or in principle (711). These networks are limited in scope and do not support the broad range of public and private needs filled by the network we describe. We favor a single distributed network with multiple usesfor example, one that could be used to study comparative clinical effectiveness and the diffusion of medical technologiesover multiple independent and single-purpose networks. A multipurpose network would reduce the burden on data holders of participating in multiple networks, as well as that on network developers of creating and maintaining redundant infrastructure. The framework that we describe suggests how we could develop a national network with broad capabilities. How Would a National Distributed Health Data Network Work? In the simplest national distributed health data network, each data holder creates a copy of their data (a network datamart) that adheres to a common data model, thus ensuring identical file structures, data fields, and coding systems. Several common data models already exist (10, 1217). The Figure illustrates the basic flow of network operations. Authorized users submit queries by means of a secure Web site. Data holders set authorization policies for each user and query type and can require approvals from privacy boards and institutional review boards. The network interface allows nontechnical users to ask simple questions without assistance (for example, a report on the uptake of a given treatment by age, sex, and geographic region). It also allows sophisticated users to perform complex analyses (for example, comparing the rates of serious cardiovascular outcomes among patients who receive different second-line antihypertensive treatments). For many questions, transferring protected health information will not be necessary. However, it may be necessary to aggregate relatively small amounts of data for analysis. Using the network, data holders may provide limited access to full-text medical records for validation and additional details. It is usually necessary to review only a small proportion of records to confirm diagnoses or to obtain risk factor data that are not coded (such as smoking status). Figure. System operations in a distributed health network. An authorized user accesses the secure network Web site to submit queries (computer programs) to run against data in the network datamarts. The boxes at the far right depict areas under control of the data holder (data holders A through D are shown). Authorization to execute a query is under control of the data holder and can be limited to specific users and uses. Data holders retrieve queries for execution, which eliminates the need for data holders to monitor incoming requests. Query results are encrypted and returned to the central Web site, where they are processed and presented to the requester. Details of each step are recorded for auditing. Example of the Use of a Distributed Network Some research programs already use a distributed network model (10, 14, 18), which provides a relevant starting point to implement a national network. The HMO Research Network Center for Education and Research on Therapeutics has conducted many multisite studies by distributing computer programs that each site applied to a local copy of their data. The outputs are then combined to provide aggregate results. Examples of studies performed in this way include the evaluation of laboratory monitoring practices for medications (1825), the use of medications during pregnancy (2628), and the use of medications that carry a black box warning (29). Such studies provide an important evidence development function that feeds back to providers, payers, and patients. Policy Issues Development and implementation of a multipurpose, multi-institutional distributed health data network requires substantial stakeholder engagement and dedicated software development. On the basis of the previously described research studies, we recommend incremental implementation with a limited set of data holders and data types. Begin with information about eligibility for health care (such as health plan enrollment data); this would allow identification of defined populations, which are important for many uses. Initial data should also include demographic characteristics; diagnosis, procedure, and pharmacy dispensing data (30); and, potentially, electronic health record data, such as vital signs. During initial implementation, pilot testing is needed to assess network design, software development, and development and implementation of the common data model. A distributed networks viability depends on both its governance mechanisms and sustained funding. A governance institution is needed to develop and oversee procedures for requesting use of the network; to set priorities; and to audit use for compliance with various security, privacy, human subject research, and proprietary concerns. Such an institution should also monitor research integrity, data integrity, conflict of interest policies, transparency of activity and results, policies related to access and use, reproducibility, publishing rights, and dispute resolution. Annual development and maintenance costs would probably be several tens of millions of dollars for an initial system that covers up to 100 million persons. This would be similar to the 3-year startup cost for the National Cancer Institutes Cancer Biomedical Informatics Grid, which totaled

Identifying potential adverse effects using the web: A new approach to medical hypothesis generation

60 million for fiscal years 2004 to 2006 (31). The National Cancer Institute fiscal year 2010 budget requests

An injury prevention program in an urban African-American community

100 million for these efforts in addition to the current funding level (32). The total annual cost of developing and maintaining a network is in line with that of individual clinical trials routinely performed to evaluate new pharmaceuticals. Although initial implementation costs are sizeable, the expected marginal costs to use the system would be small for any particular study. Various funding mechanisms are possible. Initially, we expect costs to be borne by the federal entities, whose current needs would drive network implementation. Ultimately, we believe the costs should be amortized over the systems multiple users and should support the networks expansion, functionality, and use. For example, methods could be developed for linking to the National Death Index or identifying individuals for whom multiple data holders possess different kinds of information (such as pharmacy data held by one source and clinical encounter data held by another). Advances in technologies designed to link individual records over time (such as anonymous identity resolution) without exposing protected health information are especially desirable (33). Conclusion A national distributed health data network can become an important asset to improving health and health care. A common core network would offer considerable advantages that would better support the needs of multiple users, such as the U.S. Food and Drug Administration (for their Sentinel System) and the Agency for Healthcare Research and Quality (for their comparative effectiveness network), than would building individual networks for each of these uses. The similarities in data needs and uses, coupled with potential savings of time and effort, favor a single, multipurpose network. In addition, local data holder control over use and access would encourage particip

Partners for child passenger safety: a unique child-specific crash surveillance system

Medical message boards are online resources where users with a particular condition exchange information, some of which they might not otherwise share with medical providers. Many of these boards contain a large number of posts and contain patient opinions and experiences that would be potentially useful to clinicians and researchers. We present an approach that is able to collect a corpus of medical message board posts, de-identify the corpus, and extract information on potential adverse drug effects discussed by users. Using a corpus of posts to breast cancer message boards, we identified drug event pairs using co-occurrence statistics. We then compared the identified drug event pairs with adverse effects listed on the package labels of tamoxifen, anastrozole, exemestane, and letrozole. Of the pairs identified by our system, 75-80% were documented on the drug labels. Some of the undocumented pairs may represent previously unidentified adverse drug effects.

Core Content for the Subspecialty of Clinical Informatics

OBJECTIVES Injury is a major US public health problem, particularly in urban minority communities. This paper evaluates the impact of the Safe Block Project, a comprehensive injury prevention trial, on home hazards and injury prevention knowledge in a poor urban African-American community. METHODS Nine census tracts in the community were allocated to either the intervention area or the control area. The intervention, carried out by trained community outreach workers, consisted of (1) home modification for simple prevention measures, (2) home inspection accompanied by information about home hazards, and (3) education about selected injury prevention practices. Approximately 12 months after the intervention, random samples of control and intervention homes were assessed for home hazards and injury prevention knowledge. RESULTS A significantly larger proportion of intervention homes than control homes had functioning smoke detectors, syrup of ipecac, safely stored medications, and reduced electrical and tripping hazards. No consistent differences were observed between control and intervention homes on home hazards requiring major effort to correct. CONCLUSIONS There was a distinct difference between control and intervention homes with respect to safety knowledge and home hazards requiring minimal to moderate effort to correct. The Safe Block Project could serve as a model for future urban injury prevention efforts.

Effect of transdermal testosterone treatment on serum lipid and apolipoprotein levels in men more than 65 years of age

Insurance claims data were combined with telephone survey and on-site crash investigation data to create the first large scale, child-focused motor vehicle crash surveillance system in the US. Novel data management and transfer techniques were used to create a nearly real-time data collection system. In the first year of this on-going project, known as Partners for Child Passenger Safety, over 1200 children < or = 15 years of age per week were identified in crashes reported to State Farm Insurance Co. from 15 states and Washington, D.C. Partners for Child Passenger Safety is similar in its design and overall objectives to National Automotive Sampling System (NASS), the only other population-based crash surveillance system currently operating in the US.

Are Physicians Discussing Prostate Cancer Screening with Their Patients and Why or Why Not? A Pilot Study

The Core Content for Clinical Informatics defines the boundaries of the discipline and informs the Program Requirements for Fellowship Education in Clinical Informatics. The Core Content includes four major categories: fundamentals, clinical decision making and care process improvement, health information systems, and leadership and management of change. The AMIA Board of Directors approved the Core Content for Clinical Informatics in November 2008.

Learning classifier systems: New models, successful applications

PURPOSE Because the effects of androgen replacement on lipoprotein levels are uncertain, we sought to determine the effect of transdermal testosterone treatment on serum lipid and apolipoprotein levels in elderly men. SUBJECTS AND METHODS One hundred and eight healthy men more than 65 years of age who had serum testosterone concentrations >1 SD below the mean for young men were randomly assigned to receive either testosterone (54 men; 6 mg/day) or placebo (54 men) transdermally in a double-blind fashion for 36 months. Serum concentrations of lipids and apolipoproteins were measured, and cardiovascular events recorded. RESULTS Serum total cholesterol concentrations decreased in both the testosterone-treated men and placebo-treated men, but the 3-year mean (+/- SD) decreases in the two groups (testosterone treated, -17 +/- 29 mg/dL; placebo treated, -12 +/- 38 mg/dL) were not significantly different from each other (P = 0.4). Similarly, serum low-density lipoprotein (LDL) cholesterol levels decreased in both treatment groups, but the decreases in the two groups (testosterone treated, -16 +/- 24 mg/dL; placebo treated, -16 +/- 33 mg/dL) were similar (P = 1.0). Levels of high-density lipoprotein (HDL) cholesterol, triglycerides, and apolipoproteins A-I and B did not change. Lipoprotein(a) levels increased in both groups by similar amounts (testosterone treated, 3 +/- 9 mg/dL; placebo treated, 4 +/- 6 mg/dL; P = 1.0). The number of cardiovascular events was small and did not differ significantly between the testosterone-treated men (9 events) and the placebo-treated men (5 events) during the 3-year study (relative risk = 1.8; 95% confidence interval: 0.7 to 5.0). CONCLUSIONS As compared with placebo, transdermal testosterone treatment of healthy elderly men for 3 years did not affect any of the lipid or apolipoprotein parameters that we measured. The effect of testosterone treatment on cardiovascular events was unclear, because the number of events was small.