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John F P Bridges

Publications and source records attributed to John F P Bridges.

12 recordsLinked to original sources
PubMed2025

Optimizing participant and community engagement in cancer genomic sequencing research.

PURPOSE: We describe strategies implemented across research centers of the Participant Engagement and Cancer Genome Sequencing (PE-CGS) Network to optimize engagement of participants and communities in cancer genomics research. We also present consensus definitions of engagement and engagement optimization, informed by our shared experiences in the Network. METHODS: Key informant interviews and a document review identified engagement and optimization strategies across PE-CGS research centers. Findings were synthesized using qualitative content analysis. Consensus on definitions of engagement and optimization were developed through iterative review by PE-CGS members. RESULTS: PE-CGS research centers adopted tailored strategies based on community needs and scientific gaps. Engagement strategies included community-based efforts (eg, advisory boards and newsletters) and participant-focused approaches (eg, enhanced informed consent and decision support tools). Optimization strategies leveraged scientific methods (eg, randomized controlled trials and surveys) to evaluate engagement. Engagement was described as the sustained and meaningful interactions between researchers, participants, and communities. Optimization was described as the application of scientific methods to refine and improve engagement and research processes and outcomes. CONCLUSION: Engagement and optimization strategies have informed research planning, conduct, and dissemination across PE-CGS. These approaches and definitions provide a foundation for developing evidence-based practices to strengthen participant and community involvement in cancer genomics research.

Humans↗
PubMed2006

Functional status decline as a measure of adverse events in home health care: an observational study.

BACKGROUND: Research that examines the quality of home health care is complex because no gold standard exists for measuring adverse outcomes, and because the patient and clinician populations are highly heterogeneous. The objectives in this study are to develop models to predict functional decline for three indices of functional status as measures of adverse events in home health care and determine which index is most appropriate for risk-adjusting for future quality research. METHODS: Data come from the Outcomes and Assessment Information Set (OASIS) from a large urban home health care agency and other agency data. Prognostic data yields 49,437 episodes, while follow-up data yields 47,684 episodes. We tested three indices defined as substantial decline in three or more (gt3_ADLs), two or more (gt2_ADLs), and one or more (gt1_ADLs) ADLs. Multivariate logistic regression determines the performance of the models for each index as measured by the c-statistic and Hosmer-Lemeshow chi square (chi2). RESULTS: Frequencies for gt3_ADLs, gt2_ADLs, and gt1_ADLs are 212 (0.43%), 783 (1.58%), and 4,271 (8.64%) respectively. Follow-up results are comparable with frequencies of 218 (0.46%), 763 (1.60%), and 3,949 (8.28%) for each index. Gt3_ADLs does not produce valid models. The model for gt2_ADLs consistently yields a higher c-statistic compared to gt1_ADLs (0.754 vs. 0.679, respectively). Both indices' models yield non-significant Hosmer-Lemeshow chi square indicating reasonable model fit. Findings for gt2_ADLs and gt1_ADLs are consistent over time as indicated by follow-up data results. CONCLUSION: Gt2_ADLs yields the best models as indicated by a high c-statistic and a non-significant Hosmer-Lemeshow chi2, both of which exhibit exceptional consistency. We conclude that gt2_ADLs may be preferable in defining ADL adverse events in the context of home health care.

Activities of Daily Living↗
PubMed2006

Overcoming bias in estimating the volume-outcome relationship.

OBJECTIVE: To examine the effect of hospital volume on 30-day mortality for patients with congestive heart failure (CHF) using administrative and clinical data in conventional regression and instrumental variables (IV) estimation models. DATA SOURCES: The primary data consisted of longitudinal information on comorbid conditions, vital signs, clinical status, and laboratory test results for 21,555 Medicare-insured patients aged 65 years and older hospitalized for CHF in northeast Ohio in 1991-1997. STUDY DESIGN: The patient was the primary unit of analysis. We fit a linear probability model to the data to assess the effects of hospital volume on patient mortality within 30 days of admission. Both administrative and clinical data elements were included for risk adjustment. Linear distances between patients and hospitals were used to construct the instrument, which was then used to assess the endogeneity of hospital volume. PRINCIPAL FINDINGS: When only administrative data elements were included in the risk adjustment model, the estimated volume-outcome effect was statistically significant (p=.029) but small in magnitude. The estimate was markedly attenuated in magnitude and statistical significance when clinical data were added to the model as risk adjusters (p=.39). IV estimation shifted the estimate in a direction consistent with selective referral, but we were unable to reject the consistency of the linear probability estimates. CONCLUSIONS: Use of only administrative data for volume-outcomes research may generate spurious findings. The IV analysis further suggests that conventional estimates of the volume-outcome relationship may be contaminated by selective referral effects. Taken together, our results suggest that efforts to concentrate hospital-based CHF care in high-volume hospitals may not reduce mortality among elderly patients.

Aged↗
PubMed2006

Accounting for tastes: a German perspective on the inclusion of patient preferences in healthcare.

Internationally, there has been a movement in medicine to better involve patients in decision making, whether it be at the individual or aggregate level. However, the German health sector has been slow to understand and accommodate the preferences of patients. This paper discusses the reasons why and attempts to highlight some of the consequences of not involving patients in healthcare decision making within the German context. We argue that a fundamental paradigm shift at all levels of the German health system, including better doctor/patient communication, is necessary for more choices to be available to patients and to better reflect patient preferences in centralised healthcare decision making. The newly created German health technology assessment agency (IQWiG; Institute for Quality and Economic Efficiency in the Health Care Sector) has indicated that they are interested in focusing on patient preferences, but there is a need for them to clarify what methods they deem suitable for doing so.

Efficiency, Organizational↗
PubMed2006

Patients' preferences for healthcare system reforms in Hungary: a conjoint analysis.

OBJECTIVES: To illustrate how conjoint analysis can be used to identify patient preferences for healthcare policies, and to measure preferences for healthcare reforms in Hungary. DATA SOURCE/STUDY SETTING: Data was collected via a mail-based survey and a direct survey administered in a rheumatology out-patient centre in Flór Ferenc County Hospital, Budapest, Hungary (n = 86). STUDY DESIGN: We designed and administered a conjoint analysis to the study population. Attributes and attribute levels were developed on the basis of key informant interviews and a literature review. Additional demographic, occupation and healthcare utilisation data were also collected using surveys. A mixed effects linear probability model was estimated holding respondent characteristics constant and correcting for clustering. DATA COLLECTION: Conjoint analysis questionnaires were administered by a physician to 50 consecutive rheumatology patients in a clinic and an additional 36 were mailed by post. PRINCIPAL FINDINGS: The response rate for the physician-administered survey was 98% (but 18% of these were excluded for inconsistent preferences) and 53% for the mail survey, leaving a final sample of 59. Regression results (R2 = 56.8%) indicated that patients preferred a health system that was not cost constrained (p = 0.003), was based on solidarity (p < 0.001) and where patients were empowered (p = 0.024). Further, they would choose a system with no choice of provider to avoid co-payments (p = 0.005). CONCLUSIONS: This study demonstrates that patients have clear preferences for healthcare system policy. In order to develop evidence-based healthcare policy and to empower patients in the healthcare system, methods such as conjoint analysis offer a simple yet theoretically grounded basis for policy making.

Adult↗
PubMed2005

Future challenges for the economic evaluation of healthcare: patient preferences, risk attitudes and beyond.

The continued growth in the economic evaluation of healthcare over the past 25 years has led to a shortage of trained health economists globally, leading to a number of universities and/or national governments developing specialised health economics programmes to train more health economists. One of the common problems with many of these training programmes is that they only educate new health economists to the Masters level, and as such they are unable to cover the many skills needed by a successful health economist. Furthermore, government and industry interests have ensured that economic evaluation is a heavily regulated environment that gives little incentive to seek further education. These two related factors (under-education and over-regulation) have lead to a situation where economic evaluation methods may adversely limit innovation of therapeutics and devices in clinical areas that perform badly when evaluated on the cost per QALY scale. The good news, however, is that the tide is turning and theoretically sound adjustments (such as risk adjustments and stated preferences) to the current paradigm are now being considered. This, of cause, is just the tip of the iceberg with other important issues such as time preference and the endogeneity of preference remaining very much under-researched areas in health. This paper concludes that many of these real-world issues, such as patient preferences, can be avoided by using artificial objective functions such as cost per QALY, but this comes at the cost of irrelevance and the misallocation of resources. If we are to meet all of the future challenges in economic evaluation in healthcare then we must focus more on advanced education and far less on the regulation of health economists.

Attitude↗
PubMed2005

A wolf dressed in sheep's clothing: perhaps quality measures are just unmeasured severity.

INTRODUCTION: While there has been much discussion in recent years concerning the construction of hospital quality indexes, researchers have often failed to adequately test these quality measures against testable hypotheses. Our objective is to create a quality index using a fixed-effects methodology (FE-score) and use the resulting index to explain price variation across hospitals and theoretically grounded hypotheses. METHODS: Medicare data (MEDPAR) are used for the risk adjustment of patient characteristics and the calculation of a quality score using a fixed-effects methodology for all US hospitals that provide coronary artery bypass graft (CABG). The resulting FE-score then serves as an independent variable, among others, to explain market prices for patients treated at a subset of the hospitals who have health insurance supplied from a self-insured employer. RESULTS: We find that the FE-score is positively correlated with prices, which is the opposite to the theory that hospitals with higher-than-expected adverse events would receive a lower price than higher quality hospitals. Other covariates such as insurance status and number of procedures do have the expected sign. CONCLUSIONS: We conclude that the positive correlation between the FE-score and prices demonstrates that it is behaving more like a severity scale. This indicates either an inability to isolate true quality using administrative data (i.e. incomplete risk adjustment) or a possible market failure.

Aged↗
PubMed2004

Cost-effectiveness of cardiac resynchronization therapy in patients with symptomatic heart failure.

BACKGROUND: Heart failure is a common, costly, and debilitating illness. Resynchronization of ventricular contraction in patients with heart failure improves ejection fraction. The long-term morbidity and costs associated with such cardiac resynchronization therapy remain unclear. OBJECTIVE: To assess the incremental cost-effectiveness of cardiac resynchronization therapy. DESIGN: Markov model with Monte Carlo simulation. Future costs and effects were discounted at 3%. DATA SOURCES: Effects data were obtained from a concurrent systematic review. Health-related quality-of-life and cost data were obtained from publicly available data or from surveys. TARGET POPULATION: Patients with reduced ventricular function and prolonged QRS. TIME HORIZON: Lifetime. PERSPECTIVE: U.S. health care system. INTERVENTIONS: Cardiac resynchronization therapy versus medical therapy. OUTCOME MEASURES: Quality-adjusted life-years (QALYs), costs, and incremental cost-effectiveness. RESULTS OF BASE-CASE ANALYSIS: Medical therapy yielded a median of 2.64 (interquartile range, 2.47 to 2.82) discounted QALYs and a median discounted lifetime cost of 34,400 dollars (interquartile range, 31,100 dollars to 37,700 dollars). Cardiac resynchronization therapy was associated with a median incremental cost of 107,800 dollars(interquartile range, 79,800 dollars to 156,500 dollars) per additional QALY. RESULTS OF SENSITIVITY ANALYSIS: Results were sensitive to changes in several variables, including the relative risk for death or hospitalization. LIMITATIONS: These results apply to patients who meet the inclusion criteria of the currently completed trials. CONCLUSIONS: The incremental cost per QALY for cardiac resynchronization is similar to that of other commonly used interventions but is sensitive to changes in several key variables. Resynchronization therapy should not be considered in patients with comorbid illness that shortens life expectancy.

Cardiac Pacing, Artificial↗
PubMed2004

Portfolio evaluation of health programs: a reply to Sendi et al.

Sendi et al. (Soc. Sci. Med. 57 (2003) 2207) extend previous research on cost-effectiveness analysis to the evaluation of a portfolio of interventions with risky outcomes using a "second best" approach that can identify improvements in efficiency in the allocation of resources. This method, however, cannot be used to directly identify the optimal solution to the resource allocation problem. Theoretically, a stricter adherence to the foundations of portfolio theory would permit direct optimization in portfolio selection, however, when we include uncertainty in our analysis in addition to the traditional concept of risk (which is often mislabelled uncertainty) complexities are introduced that create significant hurdles in the development of practical applications of portfolio theory for health care policy decision making.

Cost-Benefit Analysis↗
PubMed2003

Stated preference methods in health care evaluation: an emerging methodological paradigm in health economics.

One focus of health economics is the trade-off between limited resources and the (health) needs of a community. Cost-effectiveness analysis (CEA), while being one of the most accepted evaluation methodologies in health economics, does not account for many important costs and benefits of health care interventions. Some health economists have attempted to modify CEA to account for these deficiencies, while others have been working on alternative methodologies. One group of alternative methodologies can be described as stated preference techniques. These aim to measure both health and non-health outcomes (ie costs and benefits), and include qualitative analysis, conjoint analysis (often referred to as discrete choice analysis/modelling) and willingness to pay (or contingent valuation). This paper provides an overview of stated preference techniques in health economics, with particular focus on their strengths as compared with traditional evaluation methods in health care. The limitations and policy implications of these methods are also discussed.

Attitude to Health↗