Cost-Effectiveness of Capping Freeways for Use as Parks: The New York Cross-Bronx Expressway Case Study

Objectives. To examine health benefits and cost-effectiveness of implementing a freeway deck park to increase urban green space. Methods. Using the Cross-Bronx Expressway in New York City as a case study, we explored the cost-effectiveness of implementing deck parks. We built a microsimulation model that included increased exercise, fewer accidents, and less pollution as well as the cost of implementation and maintenance of the park. We estimated both the quality-adjusted life years gained and the societal costs for 2017. Results. Implementation of a deck park over sunken parts of Cross-Bronx Expressway appeared to save both lives and money. Savings were realized for 84% of Monte Carlo simulations. Conclusions. In a rapidly urbanizing world, reclaiming green space through deck parks can bring health benefits alongside economic savings over the long term. Public Health Implications. Policymakers are seeking ways to create cross-sectorial synergies that might improve both quality of urban life and health. However, such projects are very expensive, and there is little information on their return of investment. Our analysis showed that deck parks produce exceptional value when implemented over below-grade sections of road

The cost-effectiveness of limiting federal housing vouchers to use in low-poverty neighborhoods in the United States

Objective: Residents of low-income neighborhoods are exposed to relatively higher rates of crime, fewer opportunities to exercise, poorer schools, and few opportunities to eat healthy foods than residents of middle-class neighborhoods. Policies that influence neighborhood context could therefore serve as health interventions. We seek to inform the policy debate over the wisdom of spending health dollars on non-health sectors of the economy by defining the opportunity cost of doing so. Study design: Cost-effectiveness analysis with Markov model and Monte Carlo simulation. Methods: We assess the long-term health and economic benefits of Moving to Opportunity etype housing vouchers vs traditional public housing. Our Markov model draws heavily from decades of follow-up data from a large randomized-controlled trial, from which we make projections about health outcomes and costs. Results: Restricted housing vouchers cost less over the lifetime of recipients than traditional vouchers ($186,629 [95$148,856e$229,235] vs$194,077 [$153,831-$240,904]), while improving health and longevity (19.39 quality-adjusted life years [15.83 e21.35] vs 19.16 [15.65e21.03]). Over 99% of the model simulations favored restricted housing vouchers over traditional public housing or non-restrictive vouchers. Conclusions: Restrictive vouchers appear to improve population health, save money, and save lives

The Association Between Rate and Severity of Exacerbations in Chronic Obstructive Pulmonary Disease: An Application of a Joint Frailty-Logistic Model.

Exacerbations are a hallmark of chronic obstructive pulmonary disease (COPD). Evidence suggests the presence of substantial between-individual variability (heterogeneity) in exacerbation rates. The question of whether individuals vary in their tendency towards experiencing severe (versus mild) exacerbations, or whether there is an association between exacerbation rate and severity, has not yet been studied. We used data from the MACRO Study, a 1-year randomized trial of the use of azithromycin for prevention of COPD exacerbations (United States and Canada, 2006-2010; n = 1,107, mean age = 65.2 years, 59.1% male). A parametric frailty model was combined with a logistic regression model, with bivariate random effects capturing heterogeneity in rate and severity. The average rate of exacerbation was 1.53 episodes/year, with 95% of subjects having a model-estimated rate of 0.47-4.22 episodes/year. The overall ratio of severe exacerbations to total exacerbations was 0.22, with 95% of subjects having a model-estimated ratio of 0.04-0.60. We did not confirm an association between exacerbation rate and severity (P = 0.099). A unified model, implemented in standard software, could estimate joint heterogeneity in COPD exacerbation rate and severity and can have applications in similar contexts where inference on event time and intensity is considered. We provide SAS code (SAS Institute, Inc., Cary, North Carolina) and a simulated data set to facilitate further uses of this method

The Cost-Effectiveness of Lowering Permissible Noise Levels Around U.S. Airports

Aircraft noise increases the risk of cardiovascular diseases and mental illness. The allowable limit for sound in the vicinity of an airport is 65 decibels (dB) averaged over a 24-h ‘day and night’ period (DNL) in the United States. We evaluate the trade-off between the cost and the health benefits of changing the regulatory DNL level from 65 dB to 55 dB using a Markov model. The study used LaGuardia Airport (LGA) as a case study. In compliance with 55 dB allowable limit of aircraft noise, sound insulation would be required for residential homes within the 55 dB to 65 dB DNL. A Markov model was built to assess the cost-effectiveness of installing sound insulation. One-way sensitivity analyses and Monte Carlo simulation were conducted to test uncertainty of the model. The incremental cost-effectiveness ratio of installing sound insulation for residents exposed to airplane noise from LGA was $11,163/QALY gained (95$93,054/QALY gained). Changing the regulatory standard for noise exposure around airports from 65 dB to 55 dB comes at a very good value

Optimising the cost-effectiveness of speed limit enforcement cameras

Background Using the 140 speed cameras in New York City (NYC) as a case study, we explore how to optimise the number of cameras such that the most lives can be saved at the lowest cost. Methods A Markov model was built to explore the economic and health impacts of speed camera installations in NYC as well as the optimal number and placement. Both direct and indirect medical savings associated with speed cameras are weighed against their cost. Health outcomes are measured in terms of quality-adjusted life years (QALYs). Results Over the lifetime of an average NYC resident, the existing 140 speed cameras increase QALYs by 0.00044 units (95% credible interval (CrI) 0.00027 to 0.00073) and reduce costs by US$70 (95$21 to US$131) compared with no speed cameras. The return on investment would be maximised where the number of cameras more than doubled to 300. This would further increase QALY gains per resident by 0.00083 units (95$147 (95% CrI US$70 to US$221) compared with existing speed cameras. Overall, this increase in cameras would save 7000 QALYs and US$1.2 billion over the lifetime of the current cohort of New Yorkers. Conclusion Speed cameras rank among the most cost-effective social policies, saving both money and lives

America’s Declining Well-Being, Health, and Life Expectancy: Not Just a White Problem

Although recent declines in life expectancy among non-Hispanic Whites, coined “deaths of despair,” grabbed the headlines of most major media outlets, this is neither a recent problem nor is it confined to Whites. The decline in America’s health has been described in the public health literature for decades and has long been hypothesized to be attributable to an array of worsening psychosocial problems that are not specific to Whites. To test some of the dominant hypotheses, we show how various measures of despair have been increasing in the United States since 1980 and how these trends relate to changes in health and longevity. We show that mortality increases among Whites caused by the opioid epidemic come on the heels of the crack and HIV syndemic among Blacks. Both occurred on top of already higher mortality rates among all Americans relative to people in other nations, and both occurred among declines in measures of well-being. We believe that the attention given to Whites is distracting researchers and policymakers from much more serious, longer-term structural problems that affect all Americans

An Evidence-Based Policy for Managing Global Health Access Through Medical Travel

Global medical travel has had an increasing trend without a comprehensive, evidence-driven policy to ensure safe and effective practice. To identify key factors that influence medical travel, a series of studies culminating with a preference and decision-making component of over 500 prospective medical travelers from a number of countries. Results indicated that quality of care was the most critical factor in the decision, followed by lower costs of procedure and shorter waiting times. Lower costs were less of a factor if the procedure was more invasive, which also increased the importance of waiting time in the decision. The most desired destinations for care were in Europe (United Kingdom, Germany) and North America (United States). Building on these insights and previous literature, we present a model that for implementing applications from these factors and additional insights generated across the series of studies toward an effective policy framework

Impact of different interventions on preventing suicide and suicide attempt among children and adolescents in the United States: a microsimulation model study

IntroductionDespite considerable investment in suicide prevention since 2001, there is limited evidence for the effect of suicide prevention interventions among children and adolescents. This study aimed to estimate the potential population impact of different interventions in preventing suicide-related behaviors in children and adolescents.MethodsA microsimulation model study used data from national surveys and clinical trials to emulate the dynamic processes of developing depression and care-seeking behaviors among a US sample of children and adolescents. The simulation model examined the effect of four hypothetical suicide prevention interventions on preventing suicide and suicide attempt in children and adolescents as follows: (1) reduce untreated depression by 20, 50, and 80% through depression screening; (2) increase the proportion of acute-phase treatment completion to 90% (i.e., reduce treatment attrition); (3) suicide screening and treatment among the depressed individuals; and (4) suicide screening and treatment to 20, 50, and 80% of individuals in medical care settings. The model without any intervention simulated was the baseline. We estimated the difference in the suicide rate and risk of suicide attempts in children and adolescents between baseline and different interventions.ResultsNo significant reduction in the suicide rate was observed for any of the interventions. A significant decrease in the risk of suicide attempt was observed for reducing untreated depression by 80%, and for suicide screening to individuals in medical settings as follows: 20% screened: −0.68% (95% credible interval (CI): −1.05%, −0.56%), 50% screened: −1.47% (95% CI: −2.00%, −1.34%), and 80% screened: −2.14% (95% CI: −2.48%, −2.08%). Combined with 90% completion of acute-phase treatment, the risk of suicide attempt changed by −0.33% (95% CI: −0.92%, 0.04%), −0.56% (95% CI: −1.06%, −0.17%), and −0.78% (95% CI: −1.29%, −0.40%) for reducing untreated depression by 20, 50, and 80%, respectively. Combined with suicide screening and treatment among the depressed, the risk of suicide attempt changed by −0.27% (95% CI: −0.dd%, −0.16%), −0.66% (95% CI: −0.90%, −0.46%), and −0.90% (95% CI: −1.10%, −0.69%) for reducing untreated depression by 20, 50, and 80%, respectively.ConclusionReducing undertreatment (the untreated and dropout) of depression and suicide screening and treatment in medical care settings may be effective in preventing suicide-related behaviors in children and adolescents

Mathematical decision-analytic modelling to evaluate economic and health challenges in asthma and chronic obstructive pulmonary disease

Background: Reducing the burden associated with asthma and chronic obstructive pulmonary disease (COPD) requires addressing challenging care gaps. Mathematical decision-analytic models are among the best tools to address such challenges. Objectives: My overall aim in this thesis was to identify cost-effective treatments in asthma, and to quantify the value of personalizing treatments in COPD. These goals led to four specific objectives: 1) To inform the economic and health impact of improving adherence to the standard controller medications in asthma; 2) To assess the cost-effectiveness step-up treatment options for severe asthma patients; 3) To build a framework for individualized prediction of lung function in COPD; and 4) To quantify the value of personalizing COPD treatments. Methods: Cohort-based models were used to quantify the benefit of improving adherence to controller medications and evaluating the cost-effectiveness of treatments for severe asthma. Mixed-effects regression with external validation was undertaken to project lung function decline up to 11 years for COPD. Microsimulation was used to fully incorporate disease heterogeneity to evaluate the return on investment from individualizing treatments in COPD. All modeling studies were based on careful evidence synthesis and original data analyses whenever required. Results: Improving adherence to controller medications in asthma results in a gain of 0.13 quality-adjusted life years (QALYs) at the incremental cost of $3,187 per patient over 10 years. Even with full adherence, 23$78,700/QALY. Clinical variables explain 88% of variability in lung function decline. The efforts towards individualizing treatments based on patients’ clinical traits would be associated with an additional $1,265 net benefit per person. Conclusion: The analyses in this thesis demonstrate the value of mathematical simulation models in evaluating the outcomes of policies and scenarios. It is unlikely that any empirical research per se would be able to provide the insight generated in this thesis regarding the identified care gaps. Mathematical models can not only be used to evaluate the outcomes associated with specific interventions, but also to objectively document the return on investment in personalized medicine.Medicine, Faculty ofExperimental Medicine, Division ofMedicine, Department ofGraduat