Faculty Bibliography

BACKGROUND: HIV remains a major cause of preventable morbidity and mortality in Kenya. The effects of behaviors that accompany unhealthy alcohol consumption are a pervasive risk factor for HIV transmission and progression. Our objective was to estimate the portion of HIV infections attributable to unhealthy alcohol use and to evaluate the impact of hypothetical interventions directed at unhealthy alcohol use on HIV infections and deaths. METHODS: We estimated outcomes over a time horizon of 20 years using a computer simulation of the Kenyan population. This computer simulation integrates a compartmental model of HIV transmission with a mechanistic model of HIV progression that was previously validated in sub-Saharan Africa. Integration of the transmission and progression models allows simultaneous consideration of alcohol's effects on HIV transmission and progression (e.g., lowering antiretroviral adherence may increase transmission risk by elevating viral load, and may simultaneously increase progression by increasing the likelihood of AIDS). The simulation considers important aspects of heterogeneous sexual mixing patterns, including assortativeness of partners by age and activity level, age-discordant relationships, and high activity subgroups. Outcomes included number of new HIV infections, number of AIDS deaths, and infectivity (number of new infections per infected person per year). RESULTS: Our model estimated that the effects of behaviors accompanying unhealthy alcohol consumption are responsible for 13.0% of new HIV infections in Kenya. An alcohol intervention with effectiveness similar to that observed in a published randomized controlled trial of a cognitive-behavioral therapy-based intervention in Kenya (45% reduction in unhealthy alcohol consumption) could prevent nearly half of these infections, reducing their number by 69,858 and reducing AIDS deaths by 17,824 over 20 years. Estimates were sensitive to assumptions with respect to the magnitude of alcohol's underlying effects on condom use, antiretroviral therapy adherence, and sexually transmitted infection prevalence. CONCLUSIONS: A substantial number of new HIV infections in Kenya are attributable to unhealthy alcohol use. An alcohol intervention with the effectiveness observed in a published randomized controlled trial has the potential to reduce infections over 20 years by nearly 5% and avert nearly 18,000 deaths related to HIV.

BACKGROUND: Previous studies have suggested that weekend hospital care is inferior to weekday care and that this difference may be related to diminished care intensity. The purpose of this study was to determine whether a metric for measuring intensity of hospital care based on utilization of the electronic health record (EHR) was associated with patient-level outcomes. METHODS: We performed a cohort study of hospitalizations at an academic medical center. Intensity of care was defined as the hourly number of provider accessions of the electronic health record, termed "EHR interactions." Hospitalizations were categorized based on the mean difference in EHR interactions between the first Friday and Saturday of hospitalization. We used regression models to determine the association of these categories with patient outcomes after adjusting for covariates. RESULTS: EHR interactions decreased from Friday to Saturday in 77% of the 9,051 hospitalizations included in the study. As compared to hospitalizations with no change in Friday to Saturday EHR interactions, the relative lengths of stay for hospitalizations with a small, moderate, and large decrease in EHR interactions were 1.05 (95% CI 1.00-1.10), 1.11 (95% CI 1.05-1.17), and 1.25 (95% CI 1.15-1.35), respectively. Although a large decrease in EHR interactions was associated with in-hospital mortality, these findings were not significant after risk adjustment (odds ratio 1.74, 95% CI 0.93-3.25). CONCLUSIONS: Intensity of inpatient care, measured by EHR interactions, significantly diminished from Friday to Saturday, and this decrease was associated with length of stay. Hospitals should consider monitoring and correcting temporal fluctuations in care intensity.

Health system leaders sometimes adopt quality metrics without robust supporting evidence of improvements in quality and/or quantity of life, which may impair rather than facilitate improved health outcomes. In brief, there is now no easy way to measure how much "health" is conferred by a health system. However, we argue that this goal is achievable. Health-weighted composite quality metrics have the potential to measure "health" by synthesizing individual evidence-based quality metrics into a summary measure, utilizing relative weightings that reflect the relative amount of health benefit conferred by each constituent quality metric. Previously, it has been challenging to create health-weighted composite quality metrics because of methodological and data limitations. However, advances in health information technology and mathematical modeling of disease progression promise to help mitigate these challenges by making patient-level data (eg, from the electronic health record and mobile health (mHealth) more accessible and more actionable for use. Accordingly, it may now be possible to use health information technology to calculate and track a health-weighted composite quality metric for each patient that reflects the health benefit conferred to that patient by the health system. These health-weighted composite quality metrics can be employed for a multitude of important aims that improve health outcomes, including quality evaluation, population health maximization, health disparity attenuation, panel management, resource allocation, and personalization of care. We describe the necessary attributes, the possible uses, and the likely limitations and challenges of health-weighted composite quality metrics using patient-level health data.

Aim: Advance directives are often under-informed due to a lack of disease-specific prognostic information. Without well-informed advance directives patients may receive default care that is incongruent with their preferences. We aimed to further inform advance care planning in patients with severe chronic obstructive pulmonary disease by estimating outcomes with alternative advance directives. Methods: We designed a Markov microsimulation model estimating outcomes for patients choosing between the Full Code advance directive (permitting invasive mechanical ventilation), and the Do Not Intubate directive (only permitting noninvasive ventilation). Results: Our model estimates Full Code patients have marginally increased one-year survival after admission for severe respiratory failure, but are more likely to be residing in a nursing home and have frequent rehospitalizations for respiratory failure. Conclusion: Patients with severe chronic obstructive pulmonary disease may consider these potential tradeoffs between survival, rehospitalizations and institutionalization when making informed advance care plans and end-of-life decisions. We highlight outcomes research needs for variables most influential to the model's outcomes, including the risk of complications of invasive mechanical ventilation and failing noninvasive mechanical ventilation.