Effectiveness of Face Masks in Reducing the Spread of COVID-19: A Model-Based Analysis.
Medical decision making : an international journal of the Society for Medical Decision Making
BACKGROUND: The World Health Organization and US Centers for Disease Control and Prevention recommend that both infected and susceptible people wear face masks to protect against COVID-19.METHODS: We develop a dynamic disease model to assess the effectiveness of face masks in reducing the spread of COVID-19, during an initial outbreak and a later resurgence, as a function of mask effectiveness, coverage, intervention timing, and time horizon. We instantiate the model for the COVID-19 outbreak in New York, with sensitivity analyses on key natural history parameters.RESULTS: During the initial epidemic outbreak, with no social distancing, only 100% coverage of masks with high effectiveness can reduce the effective reproductive number Re below 1. During a resurgence, with lowered transmission rates due to social distancing measures, masks with medium effectiveness at 80% coverage can reduce Re below 1 but cannot do so if individuals relax social distancing efforts. Full mask coverage could significantly improve outcomes during a resurgence: with social distancing, masks with at least medium effectiveness could reduce Re below 1 and avert almost all infections, even with intervention fatigue. For coverage levels below 100%, prioritizing masks that reduce the risk of an infected individual from spreading the infection rather than the risk of a susceptible individual from getting infected yields the greatest benefit.LIMITATIONS: Data regarding COVID-19 transmission are uncertain, and empirical evidence on mask effectiveness is limited. Our analyses assume homogeneous mixing, providing an upper bound on mask effectiveness.CONCLUSIONS: Even moderately effective face masks can play a role in reducing the spread of COVID-19, particularly with full coverage, but should be combined with social distancing measures to reduce Re below 1.[Box: see text].
View details for DOI 10.1177/0272989X211019029
View details for PubMedID 34041970
EFFECTIVENESS OF FACE MASKS IN REDUCING THE SPREAD OF COVID-19: A MODEL-BASED ANALYSIS
SAGE PUBLICATIONS INC. 2021: E20-E21
View details for Web of Science ID 000648637500029
Optimal allocation of limited vaccine to control an infectious disease: Simple analytical conditions.
When allocating limited vaccines to control an infectious disease, policy makers frequently have goals relating to individual health benefits (e.g., reduced morbidity and mortality) as well as population-level health benefits (e.g., reduced transmission and possible disease eradication). We consider the optimal allocation of a limited supply of a preventive vaccine to control an infectious disease, and four different allocation objectives: minimize new infections, deaths, life years lost, or quality-adjusted life years (QALYs) lost due to death. We consider an SIR model with n interacting populations, and a single allocation of vaccine at time 0. We approximate the model dynamics to develop simple analytical conditions characterizing the optimal vaccine allocation for each objective. We instantiate the model for an epidemic similar to COVID-19 and consider n=2 population groups: one group (individuals under age 65) with high transmission but low mortality and the other group (individuals age 65 or older) with low transmission but high mortality. We find that it is optimal to vaccinate younger individuals to minimize new infections, whereas it is optimal to vaccinate older individuals to minimize deaths, life years lost, or QALYs lost due to death. Numerical simulations show that the allocations resulting from our conditions match those found using much more computationally expensive algorithms such as exhaustive search. Sensitivity analysis on key parameters indicates that the optimal allocation is robust to changes in parameter values. The simple conditions we develop provide a useful means of informing vaccine allocation decisions for communicable diseases.
View details for DOI 10.1016/j.mbs.2021.108621
View details for PubMedID 33915160
Health outcomes and cost-effectiveness of diversion programs for low-level drug offenders: A model-based analysis.
2020; 17 (10): e1003239
BACKGROUND: Cycles of incarceration, drug abuse, and poverty undermine ongoing public health efforts to reduce overdose deaths and the spread of infectious disease in vulnerable populations. Jail diversion programs aim to divert low-level drug offenders toward community care resources, avoiding criminal justice costs and disruptions in treatment for HIV, hepatitis C virus (HCV), and drug abuse. We sought to assess the health benefits and cost-effectiveness of a jail diversion program for low-level drug offenders.METHODS AND FINDINGS: We developed a microsimulation model, calibrated to King County, Washington, that captured the spread of HIV and HCV infections and incarceration and treatment systems as well as preexisting interventions such as needle and syringe programs and opiate agonist therapy. We considered an adult population of people who inject drugs (PWID), people who use drugs but do not inject (PWUD), men who have sex with men, and lower-risk heterosexuals. We projected discounted lifetime costs and quality-adjusted life years (QALYs) over a 10-year time horizon with and without a jail diversion program and calculated resulting incremental cost-effectiveness ratios (ICERs) from the health system and societal perspectives. We also tracked HIV and HCV infections, overdose deaths, and jail population size. Over 10 years, the program was estimated to reduce HIV and HCV incidence by 3.4% (95% CI 2.7%-4.0%) and 3.3% (95% CI 3.1%-3.4%), respectively, overdose deaths among PWID by 10.0% (95% CI 9.8%-10.8%), and jail population size by 6.3% (95% CI 5.9%-6.7%). When considering healthcare costs only, the program cost $25,500/QALY gained (95% CI $12,600-$48,600). Including savings from reduced incarceration (societal perspective) improved the ICER to $6,200/QALY gained (95% CI, cost-saving $24,300). Sensitivity analysis indicated that cost-effectiveness depends on diversion program participants accessing community programs such as needle and syringe programs, treatment for substance use disorder, and HIV and HCV treatment, as well as diversion program cost. A limitation of the analysis is data availability, as fewer data are available for diversion programs than for more established interventions aimed at people with substance use disorder. Additionally, like any model of a complex system, our model relies on simplifying assumptions: For example, we simplified pathways in the healthcare and criminal justice systems, modeled an average efficacy for substance use disorder treatment, and did not include costs associated with homelessness, unemployment, and breakdown in family structure.CONCLUSIONS: We found that diversion programs for low-level drug offenders are likely to be cost-effective, generating savings in the criminal justice system while only moderately increasing healthcare costs. Such programs can reduce incarceration and its associated costs, and also avert overdose deaths and improve quality of life for PWID, PWUD, and the broader population (through reduced HIV and HCV transmission).
View details for DOI 10.1371/journal.pmed.1003239
View details for PubMedID 33048929
Target Based Care: An Intervention to Reduce Variation in Postoperative Length of Stay.
The Journal of pediatrics
OBJECTIVES: To derive care targets and evaluate the impact of displaying them at the point of care on postoperative length of stay (LOS).STUDY DESIGN: A prospective cohort study using 2 years of historical controls within a freestanding, academic children's hospital. Patients undergoing benchmark cardiac surgery between May 4, 2014 and August 15, 2016 (preintervention) and September 6, 2016 to September 30, 2018 (postintervention) were included. The intervention consisted of displaying at the point of care targets for the timing of extubation, transfer from the intensive care unit (ICU), and hospital discharge. Family satisfaction, reintubation, and readmission rates were tracked.RESULTS: The postintervention cohort consisted of 219 consecutive patients. There was a reduction in variation for ICU (difference in SD -2.56, p < 0.01), and total LOS (difference in SD -2.84, P < .001). Patients stayed on average 0.97 fewer days (p<0.001) in the ICU (median -1.01 [IQR -2.15,-0.39], 0.7 fewer days (p<0.001) on mechanical ventilation (median -0.54 [IQR -0.77,-0.50], and 1.18 fewer days (p<0.001) for the total LOS (median -2.25 [IQR -3.69,-0.15]. Log transformed multivariable linear regression demonstrated the intervention to be associated with shorter ICU LOS (beta coefficient -0.19, SE 0.059, p<0.001), total postoperative LOS (beta coefficient -0.12, SE 0.052, p=0.02), and ventilator duration (beta coefficient -0.21, SE 0.048, p<0.001). Balancing metrics did not differ after the intervention.CONCLUSIONS: Target based care is a simple, novel intervention associated with reduced variation in LOS and absolute LOS across a diverse spectrum of complex cardiac surgeries.
View details for DOI 10.1016/j.jpeds.2020.09.017
View details for PubMedID 32920104
PREDICTING PATIENT-LEVEL ANTIBIOTIC ADHERENCE
SAGE PUBLICATIONS INC. 2020: E45–E46
View details for Web of Science ID 000509275600049
Predicting and improving patient-level antibiotic adherence.
Health care management science
Low adherence to prescribed medications causes substantial health and economic burden. We analyzed primary data from electronic medical records of 250,000 random patients from Israel's Maccabi Healthcare services from 2007 to 2017 to predict whether a patient will purchase a prescribed antibiotic. We developed a decision model to evaluate whether an intervention to improve purchasing adherence is warranted for the patient, considering the cost of the intervention and the cost of non-adherence. The best performing prediction model achieved an average area under the receiver operating characteristic curve (AUC) of 0.684, with 82% accuracy in detecting individuals who had less than 50% chance of purchasing a prescribed drug. Using the decision model, an adherence intervention targeted to patients whose predicted purchasing probability is below a specified threshold can increase the number of prescriptions filled while generating significant savings compared to no intervention - on the order of 6.4% savings and 4.0% more prescriptions filled for our dataset. We conclude that analysis of large-scale patient data from electronic medical records can help predict the probability that a patient will purchase a prescribed antibiotic and can provide real-time predictions to physicians, who can then counsel the patient about medication importance. More broadly, in-depth analysis of patient-level data can help shape the next generation of personalized interventions.
View details for DOI 10.1007/s10729-020-09523-3
View details for PubMedID 33017035
- Cost-Effectiveness Analysis of Fenestrated Endovascular Aneurysm Repair Compared With Open Surgical Repair for Patients With Juxtarenal Abdominal Aortic Aneurysms MOSBY-ELSEVIER. 2019: E244–E245