All Public Health is Local: Lessons From Eagle County During the First 2 Years of the Coronavirus Disease-2019 Pandemic.

BACKGROUND: During the coronavirus disease-2019 (COVID-19) pandemic cumulative United States COVID-19 deaths per capita were higher than all other large, high-income nations, but with substantial variation across the country. OBJECTIVE: The aim was to detail the public health response during the pandemic in Eagle County, Colorado. RESEARCH DESIGN AND MEASURES: Observational study using pre-COVID-19 county public health metrics. Pandemic actions were recorded from a narrative summary of daily phone consultations by a county-wide taskforce and interviews. Outcomes obtained from local, state, and nationally reported databases. METHODS: Eagle County began with a life expectancy of 85.9, low all-cause age-adjusted death rates equal for both White and Latinx populations, a high household median income, and other prepandemic advantages. It also launched an innovative, independent county-wide taskforce lead by experienced mid-level managers. The taskforce implemented rapid communication of decision consequences, made immediate course corrections without traditional organizational approvals or contradictory political pressures. RESULTS: Eagle County was first in Colorado to obtain Personal Protective Equipment and to establish a drive-through testing facility. The COVID-19 case fatality rate was 0.34%. The sole intensive care unit never reached maximum capacity. By March 2022, Eagle County had administered at least 1 vaccine dose to 100% of the population and 83% were fully vaccinated. CONCLUSIONS: It is not possible to directly attribute superior outcomes to either the baseline characteristics of Eagle County or its innovative taskforce design and deployment. Rather this report highlights the potential impact that improving the baseline health status of US citizens and permitting novel problem-solving approaches by local public health officials might have for the next pandemic.

Changes in hospital mortality for United States intensive care unit admissions from 1988 to 2012.

INTRODUCTION: A decrease in disease-specific mortality over the last twenty years has been reported for patients admitted to United States (US) hospitals, but data for intensive care patients are lacking. The aim of this study was to describe changes in hospital mortality and case-mix using clinical data for patients admitted to multiple US ICUs over the last 24 years. METHODS: We carried out a retrospective time series analysis of hospital mortality using clinical data collected from 1988 to 2012. We also examined the impact of ICU admission diagnosis and other clinical characteristics on mortality over time. The potential impact of hospital discharge destination on mortality was also assessed using data from 2001 to 2012. RESULTS: For 482,601 ICU admissions there was a 35% relative decrease in mortality from 1988 to 2012 despite an increase in age and severity of illness. This decrease varied greatly by diagnosis. Mortality fell by >60% for patients with chronic obstructive pulmonary disease, seizures and surgery for aortic dissection and subarachnoid hemorrhage. Mortality fell by 51% to 59% for six diagnoses, 41% to 50% for seven diagnoses, and 10% to 40% for seven diagnoses. The decrease in mortality from 2001 to 2012 was accompanied by an increase in discharge to post-acute care facilities and a decrease in discharge to home. CONCLUSIONS: Hospital mortality for patients admitted to US ICUs has decreased significantly over the past two decades despite an increase in the severity of illness. Decreases in mortality were diagnosis specific and appear attributable to improvements in the quality of care, but changes in discharge destination and other confounders may also be responsible.

Building the evidence base for decision making in cancer genomic medicine using comparative effectiveness research.

The clinical utility is uncertain for many cancer genomic applications. Comparative effectiveness research (CER) can provide evidence to clarify this uncertainty. The aim of this study was to identify approaches to help stakeholders make evidence-based decisions and to describe potential challenges and opportunities in using CER to produce evidence-based guidance. We identified general CER approaches for genomic applications through literature review, the authors' experiences, and lessons learned from a recent, seven-site CER initiative in cancer genomic medicine. Case studies illustrate the use of CER approaches. Evidence generation and synthesis approaches used in CER include comparative observational and randomized trials, patient-reported outcomes, decision modeling, and economic analysis. Significant challenges to conducting CER in cancer genomics include the rapid pace of innovation, lack of regulation, and variable definitions and evidence thresholds for clinical and personal utility. Opportunities to capitalize on CER methods in cancer genomics include improvements in the conduct of evidence synthesis, stakeholder engagement, increasing the number of comparative studies, and developing approaches to inform clinical guidelines and research prioritization. CER offers a variety of methodological approaches that can address stakeholders' needs and help ensure an effective translation of genomic discoveries.

A Case-Control Study to Add Volumetric or Clinical Mammographic Density into the Tyrer-Cuzick Breast Cancer Risk Model.

BACKGROUND: Accurate breast cancer risk assessment for women attending routine screening is needed to guide screening and preventive interventions. We evaluated the accuracy of risk predictions from both visual and volumetric mammographic density combined with the Tyrer-Cuzick breast cancer risk model. METHODS: A case-control study (474 patient participants and 2243 healthy control participants) of women aged 40-79 years was performed using self-reported classical risk factors. Breast density was measured by using automated volumetric software and Breast Imaging and Reporting Data System (BI-RADS) density categories. Odds ratios (95% CI) were estimated by using logistic regression, adjusted for age, demographic factors, and 10-year risk from the Tyrer-Cuzick model, for a change from the 25th to 75th percentile of the adjusted percent density distribution in control participants (IQ-OR). RESULTS: After adjustment for classical risk factors in the Tyrer-Cuzick model, age, and body mass index (BMI), BI-RADS density had an IQ-OR of 1.55 (95% CI = 1.33 to 1.80) compared with 1.40 (95% CI = 1.21 to 1.60) for volumetric percent density. Fibroglandular volume (IQ-OR = 1.28, 95% CI = 1.12 to 1.47) was a weaker predictor than was BI-RADS density (Pdiff = 0.014) or volumetric percent density (Pdiff = 0.065). In this setting, 4.8% of women were at high risk (8% + 10-year risk), using the Tyrer-Cuzick model without density, and 7.1% (BI-RADS) compared with 6.8% (volumetric) when combined with density. CONCLUSION: The addition of volumetric and visual mammographic density measures to classical risk factors improves risk stratification. A combined risk could be used to guide precision medicine, through risk-adapted screening and prevention strategies.

Relemed: sentence-level search engine with relevance score for the MEDLINE database of biomedical articles.

BACKGROUND: Receiving extraneous articles in response to a query submitted to MEDLINE/PubMed is common. When submitting a multi-word query (which is the majority of queries submitted), the presence of all query words within each article may be a necessary condition for retrieving relevant articles, but not sufficient. Ideally a relationship between the query words in the article is also required. We propose that if two words occur within an article, the probability that a relation between them is explained is higher when the words occur within adjacent sentences versus remote sentences. Therefore, sentence-level concurrence can be used as a surrogate for existence of the relationship between the words. In order to avoid the irrelevant articles, one solution would be to increase the search specificity. Another solution is to estimate a relevance score to sort the retrieved articles. However among the >30 retrieval services available for MEDLINE, only a few estimate a relevance score, and none detects and incorporates the relation between the query words as part of the relevance score. RESULTS: We have developed "Relemed", a search engine for MEDLINE. Relemed increases specificity and precision of retrieval by searching for query words within sentences rather than the whole article. It uses sentence-level concurrence as a statistical surrogate for the existence of relationship between the words. It also estimates a relevance score and sorts the results on this basis, thus shifting irrelevant articles lower down the list. In two case studies, we demonstrate that the most relevant articles appear at the top of the Relemed results, while this is not necessarily the case with a PubMed search. We have also shown that a Relemed search includes not only all the articles retrieved by PubMed, but potentially additional relevant articles, due to the extended 'automatic term mapping' and text-word searching features implemented in Relemed. CONCLUSION: By using sentence-level matching, Relemed can deliver higher specificity, thus eliminating more false-positive articles. By introducing an appropriate relevance metric, the most relevant articles on which the user wishes to focus are listed first. Relemed also shrinks the displayed text, and hence the time spent scanning the articles.

What Do Women Know About Breast Density? Results From a Population Survey of Virginia Women.

PURPOSE: Breast density reduces the sensitivity of mammography and is a moderate independent risk factor for breast cancer. Virginia is one of 24 states that currently require notification of patients when they have dense breasts. However, little is known about what women in the general population know about breast density. This survey study assessed knowledge about breast density and about its impact on mammography and breast cancer risk. METHODS: A random sample of 1024 Virginia women between ages 35 and 70 years without breast cancer, reached by landline and cell phone, who completed a 24-minute interview. Bivariate and multivariate analysis was performed. RESULTS: Thirty-six percent of respondents had been informed about their breast density. These women were more likely to be familiar with the term "breast density." Seventy-five percent of respondents reported being either somewhat or very familiar with risk factors for breast cancer, but <1% spontaneously listed breast density as a risk factor. About half of women who had a mammogram in the last year were aware of their breast density. Overall, only one in five women were aware that density reduces the sensitivity of mammography and only one in eight were aware that density increases breast cancer risk. Very few respondents (5.3%) were able to correctly answer three density knowledge questions. Lower-education, African-American, and Jewish women were less knowledgeable about breast density. CONCLUSIONS: Although women are becoming aware of the term "breast density," they may not understand its relationship to cancer detection on mammography and, especially, its relationship to breast cancer risk.

Impact of patient distance to radiation therapy on mastectomy use in early-stage breast cancer patients.

PURPOSE: Treatment access underlies quality cancer care. We hypothesize that mastectomy rates in a rural state are independently influenced by distance to radiation therapy (XRT) and by changing XRT access through opening new facilities. PATIENTS AND METHODS: Early-stage breast cancer patients diagnosed from 1996 to 2000 were identified in the Virginia state registry. Distance from patient zip code to nearest XRT facility was calculated with geographical software. Distance to XRT facility (< or = 10, > 10 to 25, > 25 to 50, and > 50 miles), American Joint Committee on Cancer tumor stage, age, race, and diagnosis year were evaluated for influencing mastectomy rate. Mastectomy use within 15 miles of five new facilities was assessed before and after opening. RESULTS: Among 20,094 patients, 43% underwent mastectomy, 53% underwent lumpectomy, and therapy of 4% of patients is unknown. Twenty-nine percent of patients lived more than 10 miles from XRT facility. Mastectomy increased with distance to XRT facility (43% at < or = 10 miles, 47% at > 10 to 25 miles, 53% at > 25 to 50 miles, and 58% at > 50 miles; P < .001). Among 11,597 patients with T1 (< 2 cm) tumors, mastectomy also varied by distance (31% at < or = 10 miles, 36% at > 10 to 25 miles, 41% at > 25 to 50 miles, and 49% at > 50 miles; P < .001). In multivariate analysis, mastectomy use was independently influenced by XRT distance after adjusting for age, race, T stage, and diagnosis year. Over the study period, mastectomy rates declined from 48% to 43% across Virginia, and there were similar declines in a 15-mile area around four new radiation facilities in urban settings. However, mastectomies decreased from 61% to 45% around a new XRT facility in a rural setting. CONCLUSION: Distance to XRT facility significantly impacts mastectomy use. Opportunities for increasing breast-conservation rates through improved XRT access exist.

Locating previously unknown patterns in data-mining results: a dual data- and knowledge-mining method.

BACKGROUND: Data mining can be utilized to automate analysis of substantial amounts of data produced in many organizations. However, data mining produces large numbers of rules and patterns, many of which are not useful. Existing methods for pruning uninteresting patterns have only begun to automate the knowledge acquisition step (which is required for subjective measures of interestingness), hence leaving a serious bottleneck. In this paper we propose a method for automatically acquiring knowledge to shorten the pattern list by locating the novel and interesting ones. METHODS: The dual-mining method is based on automatically comparing the strength of patterns mined from a database with the strength of equivalent patterns mined from a relevant knowledgebase. When these two estimates of pattern strength do not match, a high "surprise score" is assigned to the pattern, identifying the pattern as potentially interesting. The surprise score captures the degree of novelty or interestingness of the mined pattern. In addition, we show how to compute p values for each surprise score, thus filtering out noise and attaching statistical significance. RESULTS: We have implemented the dual-mining method using scripts written in Perl and R. We applied the method to a large patient database and a biomedical literature citation knowledgebase. The system estimated association scores for 50,000 patterns, composed of disease entities and lab results, by querying the database and the knowledgebase. It then computed the surprise scores by comparing the pairs of association scores. Finally, the system estimated statistical significance of the scores. CONCLUSION: The dual-mining method eliminates more than 90% of patterns with strong associations, thus identifying them as uninteresting. We found that the pruning of patterns using the surprise score matched the biomedical evidence in the 100 cases that were examined by hand. The method automates the acquisition of knowledge, thus reducing dependence on the knowledge elicited from human expert, which is usually a rate-limiting step.

Data mining and clinical data repositories: Insights from a 667,000 patient data set.

Clinical repositories containing large amounts of biological, clinical, and administrative data are increasingly becoming available as health care systems integrate patient information for research and utilization objectives. To investigate the potential value of searching these databases for novel insights, we applied a new data mining approach, HealthMiner, to a large cohort of 667,000 inpatient and outpatient digital records from an academic medical system. HealthMiner approaches knowledge discovery using three unsupervised methods: CliniMiner, Predictive Analysis, and Pattern Discovery. The initial results from this study suggest that these approaches have the potential to expand research capabilities through identification of potentially novel clinical disease associations.

CXC chemokine receptor 3 expression by activated CD8+ T cells is associated with survival in melanoma patients with stage III disease.

Despite the presence of tumor Ag-specific CD8(+) T cells in the peripheral blood, metastatic melanoma often evades immune-mediated destruction. Even after therapeutic efforts to expand Ag-specific T-cell populations, the correlation between magnitude of response and clinical efficacy has been weak. Because the migratory phenotype of tumor Ag-specific effector T cells may determine their ability for tumor control, we hypothesized that the expression of CC or CXC chemokine receptor (CCR) molecules on activated CD8(+) T cells may define phenotypes associated with more effective control of melanoma progression and prolonged survival. In a retrospective evaluation of patient isolates, CCR expression was determined for activated CD8(+) T cells derived from the peripheral blood or tumor-involved lymph nodes of 52 patients with stage III or IV metastatic melanoma. In patients with stage III disease, expression of CXCR3 by CD8(+)CD45RO(+) cells was significantly associated with enhanced survival. This was a stage-specific effect, because it was not observed in patients with stage IV disease. In addition, CCR4 and CXCR3 were highly coexpressed and associated with enhanced survival in stage III patients; however, CXCR3 seems to be the dominant receptor associated with clinical outcome. These findings support the hypothesis that the host immune system affects cancer progression and control, and that measures of CCR status of circulating lymphocytes may have prognostic value.

Initial clinical validation of Health Heritage, a patient-facing tool for personal and family history collection and cancer risk assessment.

Personal and family health histories remain important independent risk factors for cancer; however they are currently not being well collected or used effectively. Health Heritage was designed to address this need. The purpose of this study was to validate the ability of Health Heritage to identify patients appropriate for further genetic evaluation and to accurately stratify cancer risk. A retrospective chart review was conducted on 100 random patients seen at an adult genetics clinic presenting with concern for an inherited predisposition to cancer. Relevant personal and family history obtained from the patients' medical records was entered into Health Heritage. Recommendations by Health Heritage were compared to national guidelines of eligibility for genetic evaluation. Agreement between Health Heritage referral for genetic evaluation and guideline eligibility for genetic evaluation was 97% (sensitivity 98% and specificity 88%). Risk stratification for cancer was also compared between Health Heritage and those documented by a geneticist. For patients at increased risk for breast, ovarian, or colorectal cancer as determined by the geneticist, risk stratification by Health Heritage agreed 90, 93, and 75%, respectively. Discordances in risk stratification were attributed to both complex situations better handled by the geneticist and Health Heritage's adherence to incorporating all information into its algorithms. Health Heritage is a clinically valid tool to identify patients appropriate for further genetic evaluation and to encourage them to confirm the assessment and management recommendations with cancer genetic experts. Health Heritage also provides an estimate of cancer risk that is complementary to a genetics team.

GeneX Va: VBC open source microarray database and analysis software.

Developed by the Virginia Bioinformatics Consortium (VBC), GeneX Va is an open source, freeware database and bioinformatics analysis software for archiving and analyzing Affymetrix GeneChip data. It provides an integrated framework for management, documentation, and analysis of microarray experiments and data to support a range of users, from individual research laboratories to institutional microarray facilities. GeneX Va also provides web-based access to a PostgreSQL relational database system with a comprehensive security system. Data can be extracted from the database and delivered to interactive or scriptable statistical analysis protocols. The security system allows each investigator to manage their own array data and analysis output files and also provides custom access privileges for other users, groups, and internal/external collaborators. The analysis interface uses "Analysis Trees," an innovative user interface that allows researchers to interactively create a tree-structured flow chart of analysis routines. The latest GeneX Va software is available from and can be freely downloaded at the Sourceforge web site http://va-genex.sourceforge.net. To allow researchers to access the database and analysis capabilities of the GeneX Va system, microarray data from many VBC GeneChip experiments have been deposited into a public section of the GeneX Va system at the University of Virginia. The VBC GeneX Va sites, which include documentation, are at http://genes.med.virginia.edu/ of the University of Virginia and at http://genex.csbc.vcu.edu/ of the Virginia Commonwealth University.

Outcomes up to 5 years after severe, acute respiratory failure.

STUDY OBJECTIVE: To use an existing database from a large cohort study with follow-up as long as 5.5 years to assess the extended prognosis of patients who survived their hospitalizations for severe acute respiratory failure (ARF). DESIGN, SETTING, AND PATIENTS: Secondary analysis of an inception cohort of 1,722 patients with ARF requiring mechanical ventilation from five major medical centers who were entered into the prospective Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatment. The 1,075 patients (62.4%) who survived hospitalization had systematic follow-up of vital status for a median time of 662 days (interquartile range, 327 to 1,049 days; range, 2 to 2,014 days). Interviews performed a median of 5 months after hospital discharge assessed functional capacity and quality of life (QOL). The main outcome measure was survival after hospital discharge. Secondary measures were functional status and QOL. Cox proportional hazard regression identified factors influencing posthospital survival. RESULTS: The median survival time after hospital discharge for ARF was > 5.3 years. The posthospital survival time was shorter for those with older age, male gender, several preexisting comorbid conditions, worse prehospital functional status, greater acute physiologic derangement, and a do-not-resuscitate order while in the hospital, and for those discharged to a location other than home. Five months after hospital discharge, 48% of survivors needed help with at least one activity of daily living, and 27% rated their QOL as poor or fair. However, most of these impairments were present before respiratory failure occurred. CONCLUSIONS: Extended survival is common among patients with ARF who require mechanical ventilation and who survive hospitalization. Among these patients, only a small fraction of the impairment in activity and QOL can be considered to be a sequela of the respiratory failure or its therapy. These findings are relevant to the care decisions for such critically ill patients.

A user-centered model for web site design: needs assessment, user interface design, and rapid prototyping.

As the Internet continues to grow as a delivery medium for health information, the design of effective Web sites becomes increasingly important. In this paper, the authors provide an overview of one effective model for Web site design, a user-centered process that includes techniques for needs assessment, goal/task analysis, user interface design, and rapid prototyping. They detail how this approach was employed to design a family health history Web site, Health Heritage . This Web site helps patients record and maintain their family health histories in a secure, confidential manner. It also supports primary care physicians through analysis of health histories, identification of potential risks, and provision of health care recommendations. Visual examples of the design process are provided to show how the use of this model resulted in an easy-to-use Web site that is likely to meet user needs. The model is effective across diverse content arenas and is appropriate for applications in varied media.

Comparative effectiveness research in cancer genomics and precision medicine: current landscape and future prospects.

A major promise of genomic research is information that can transform health care and public health through earlier diagnosis, more effective prevention and treatment of disease, and avoidance of drug side effects. Although there is interest in the early adoption of emerging genomic applications in cancer prevention and treatment, there are substantial evidence gaps that are further compounded by the difficulties of designing adequately powered studies to generate this evidence, thus limiting the uptake of these tools into clinical practice. Comparative effectiveness research (CER) is intended to generate evidence on the "real-world" effectiveness compared with existing standards of care so informed decisions can be made to improve health care. Capitalizing on funding opportunities from the American Recovery and Reinvestment Act of 2009, the National Cancer Institute funded seven research teams to conduct CER in genomic and precision medicine and sponsored a workshop on CER on May 30, 2012, in Bethesda, Maryland. This report highlights research findings from those research teams, challenges to conducting CER, the barriers to implementation in clinical practice, and research priorities and opportunities in CER in genomic and precision medicine. Workshop participants strongly emphasized the need for conducting CER for promising molecularly targeted therapies, developing and supporting an integrated clinical network for open-access resources, supporting bioinformatics and computer science research, providing training and education programs in CER, and conducting research in economic and decision modeling.